Las numerosas formas en que se pueden organizar, guardar y manejar los datos dentro de un programa de computadora se denominan estructuras de datos en Java. Estas estructuras ofrecen un método metódico para manejar y administrar datos de manera efectiva, permitiendo operaciones útiles como inserción, eliminación, recuperación y recorrido.
El artículo explorará todo lo relacionado con las estructuras de datos en Java y ayudará a los principiantes a comprenderlas de manera fácil y efectiva.
- ¿Que es Java?
- ¿Qué son las estructuras de datos en Java?
- Tipos de estructuras de datos en Java
- Ventajas de las estructuras de datos en Java
- Clasificación de estructuras de datos
- Preguntas frecuentes sobre estructuras de datos en Java
¿Que es Java?
Java es un popular lenguaje de programación orientado a objetos reconocido por su amplia biblioteca estándar y libertad de plataforma. Ofrece una arquitectura sólida para crear programas que se ejecutan sin recompilación en varias plataformas. La conocida biblioteca para Java tiene una variedad de sistemas de archivos que permiten manejar numerosos tipos de datos de manera eficiente.
¿Qué son las estructuras de datos en Java?
La forma en que se organizan y almacenan los datos en la memoria de un programa de computadora depende estrechamente de las estructuras de registros de Java. La conocida biblioteca de Java incluye un tipo importante de estructuras estadísticas integradas. Algunos de los sistemas de registros que permiten a los programadores formas breves y sencillas de guardar y organizar datos incluyen listas, pilas, colas y matrices conectadas. Los desarrolladores pueden realizar rápidamente operaciones como inserción, eliminación, búsqueda y clasificación porque proporcionan una variedad de mecanismos para obtener acceso, modificar y administrar datos. Los programadores de Java pueden reducir el uso de memoria y aumentar considerablemente la eficiencia general de sus programas utilizando estas estructuras de datos.
Tipos de estructuras de datos en Java
La lista de estructuras de datos en Java que se enumeran a continuación
- matrices
- Lista de arreglo
- Lista enlazada
- Pila
- Cola
- HashMap
- Conjunto de hash
- Conjunto de árboles
- ÁrbolMapa
- Grafico
- Árbol
El siguiente diagrama explica claramente los tipos de estructuras de datos en Java.
Clasificación adicional de tipos de estructuras de datos:
Hay dos tipos de estructuras de datos: -
- Estructuras de datos primitivas
- Estructuras de datos no primitivas
1) Estructuras de datos primitivas: También conocidos como tipos de datos primitivos, estos son tipos de datos básicos integrados en Java. Incluyen:
2) Estructuras de datos no primitivas: Las estructuras de registros no primitivos son más complejas y se componen de tipos de información primitivos. Se pueden clasificar, además, en dos tipos:
Ventajas de las estructuras de datos en Java
1) matrices:
Una matriz es una estructura de datos básica y de uso frecuente en el contexto de las estructuras de datos de Java. Ofrece un método para almacenar una colección de tamaño fijo de componentes del mismo tipo. Debido a que brindan un acceso rápido y fácil a los elementos según su índice, las matrices son una herramienta crucial para administrar y organizar datos.
Ventajas:
Desventajas:
Funciones:
Implementación:
Nombre del archivo: ArrayExample.java
import java.util.*; public class ArrayExample { public static void main(String[] args) { int[] numbers={10,20,30,40,50}; // Initialize an array of integers System.out.println('Element at index 0:'+numbers[0]); System.out.println('Element at index 2:'+numbers[2]); System.out.println('Element at index 4:'+numbers[4]); int sum=0; for (int i=0;i<numbers.length;i++) { sum+="numbers[i];" } system.out.println('sum of array elements:'+sum); numbers[2]="35;" update an element in the system.out.println('updated at index 2:'+numbers[2]); system.out.println('elements array:'); for (int number:numbers) system.out.println(number); < pre> <p> <strong>Output:</strong> </p> <pre> Element at index 0:10 Element at index 2:30 Element at index 4:50 Sum of array elements:150 Updated element at index 2:35 Elements in the array: 10 20 35 40 50 </pre> <h3>2) ArrayList:</h3> <p>ArrayList in Java is a dynamic data structure that allows for the storage and manipulation of elements. It is part of the Java Collections Framework and is implemented using an array internally.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Dynamic Size:</td> Unlike arrays, ArrayLists can dynamically grow or shrink in size as elements are added or removed. It eliminates the need for manual resizing and allows for handling varying amounts of data conveniently. </tr><tr><td>Easy Element Manipulation:</td> ArrayLists offer methods to add, remove, and modify elements at any position within the list. Its flexibility simplifies common operations such as insertion, deletion, and updating, making element manipulation more efficient. </tr><tr><td>Random Access:</td> ArrayLists support random Access to elements using their index, enabling quick retrieval and modification of elements at specific positions within the list. It facilitates efficient element access and enhances overall performance. </tr><tr><td>Compatibility with Java Collection Framework:</td> ArrayLists implement the List interface, making them compatible with other Collection classes in the Java Collections Framework. Its compatibility allows for seamless integration with various algorithms and operations provided by the framework. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Higher Memory Overhead:</td> ArrayLists require additional memory to maintain their internal structure, resulting in higher memory overhead compared to arrays. It can be a concern when dealing with large collections of elements. </tr><tr><td>Slower Insertion and Deletion:</td> Inserting or deleting elements in the middle of an ArrayList requires shifting elements, which can be time-consuming for large lists. In scenarios where frequent insertion or deletion operations are expected, other data structures like LinkedList may offer better performance. </tr><tr><td>Limited Performance for Search:</td> Searching for an element in an unsorted ArrayList requires iterating over the elements until a match is found. It is a linear search approach that results in slower search performance compared to data structures optimized for searching, such as HashSet or TreeMap. </tr><tr><td>No Primitive Type Support:</td> ArrayLists can only store objects and do not directly support primitive data types like int or char. To store primitive types, wrapper classes like Integer or Character need to be used, leading to potential autoboxing and unboxing overhead. </tr></ol> <p> <strong>Functions:</strong> </p> <ol class="points"> <tr><td>Creating an ArrayList:</td> Declare and initialize an ArrayList using the ArrayList class and specify the element type within the angle brackets. </tr><tr><td>Adding Elements:</td> Use the add method to append elements at the end of the ArrayList. </tr><tr><td>Accessing Elements:</td> Use the get technique to retrieve the price of detail at a selected index. </tr><tr><td>Modifying Elements:</td> Update the cost of detail at a specific index for the usage of the set approach. </tr><tr><td>Finding Size:</td> Use the dimensions method to get the cutting-edge quantity of factors in the ArrayList. </tr><tr><td>Removing Elements:</td> Use the remove approach to delete a detail at a specific index or via providing the object reference. </tr><tr><td>Iterating through the ArrayList:</td> Use loops to iterate over each element in the ArrayList and perform operations on them. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> ArrayListExample.java</p> <pre> import java.util.*; public class ArrayListExample { public static void main(String[] args) { // Create an ArrayList to store integers ArrayList numbers=new ArrayList(List.of(10,20,30,40,50)); //Access and print elements from the ArrayList System.out.println('Element at index 0:'+numbers.get(0)); System.out.println('Element at index 2:'+numbers.get(2)); System.out.println('Element at index 4:'+numbers.get(4)); // Calculate and print the sum of all elements in the ArrayList int sum=numbers.stream().mapToInt(Integer::intValue).sum(); System.out.println('Sum of ArrayList elements:'+sum); // Update an element in the ArrayList numbers.set(2,35); System.out.println('Updated element at index 2:'+numbers.get(2)); // Iterate through the ArrayList using a for-each loop and print the elements System.out.println('Elements in the ArrayList:'); for (int number:numbers) { System.out.println(number); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Element at index 0:10 Element at index 2:30 Element at index 4:50 Sum of ArrayList elements:150 Updated element at index 2:35 Elements in the ArrayList: 10 20 35 40 50 </pre> <h3>3) Linked List:</h3> <p>A linked list is a linear data structure in which elements are stored in separate objects called nodes. A reference link to the following node in the sequence is included in each node's data element. The list's final node links to null, indicating that the list has ended.</p> <p>Unlike arrays, linked lists do not require contiguous memory allocation. Each node in a linked list can be allocated independently, allowing for dynamic memory allocation and efficient insertion and deletion operations.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Dynamic Size:</td> LinkedList can grow or shrink dynamically, making it suitable for varying or unknown data sizes. </tr><tr><td>Efficient Insertion and Deletion:</td> Inserting or deleting elements within a LinkedList is efficient, as it does not require shifting elements. </tr><tr><td>No Contiguous Memory Requirement:</td> LinkedList does not need contiguous memory allocation, making it flexible and suitable for unpredictable memory situations. </tr><tr><td>Easy Modification:</td> LinkedList allows easy modification of elements by changing reference pointers, enabling efficient manipulation. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Slower Random Access:</td> LinkedList has slower random Access as it requires traversing the list to access elements by index. </tr><tr><td>Increased Memory Overhead:</td> LinkedList requires additional memory for references and nodes, increasing memory overhead compared to arrays. </tr><tr><td>Inefficient Search:</td> LinkedList has slower search operations, requiring sequential iteration to find specific elements. </tr></ol> <p> <strong>Functions:</strong> </p> <ol class="points"> <tr><td>Creating a LinkedList:</td> Declare and initialize a LinkedList using the LinkedList class. </tr><tr><td>Adding Elements:</td> Use the add method to append elements at the end of the LinkedList. </tr><tr><td>Accessing Elements:</td> Use the get method to retrieve the value of an element at a specific index. </tr><tr><td>Modifying Elements:</td> Update the value of an element at a particular index using the set method. </tr><tr><td>Removing Elements:</td> Use the remove method to delete an element at a specific index or by providing the object reference. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> LinkedList1.java</p> <pre> import java.util.*; public class LinkedList1 { public static void main(String[] args) { // Create a LinkedList to store integers LinkedList linkedList1 = new LinkedList(); // Add elements to the LinkedList linkedList1.add(10); linkedList1.add(20); linkedList1.add(30); linkedList1.add(40); linkedList1.add(50); // Print the LinkedList System.out.println('LinkedList:'+linkedList1); // Remove an element from the LinkedList linkedList1.removeFirst(); System.out.println('LinkedList after removing first element:'+linkedList1); // Check if an element exists in the LinkedList boolean containsElement=linkedList1.contains(30); System.out.println('LinkedList contains element 30?'+containsElement); // Get the first and last elements of the LinkedList int firstElement=linkedList1.getFirst(); int lastElement=linkedList1.getLast(); System.out.println('First element:'+firstElement); System.out.println('Last element:'+lastElement); // Clear the LinkedList linkedList1.clear(); System.out.println('LinkedList after clearing:'+linkedList1); } } </pre> <p> <strong>Output:</strong> </p> <pre> LinkedList:[10, 20, 30, 40, 50] LinkedList after removing first element:[20, 30, 40, 50] LinkedList contains element 30?true First element:20 Last element:50 LinkedList after clearing:[] </pre> <h3>4) Stack:</h3> <p>The Last-In-First-Out (LIFO) principle dictates that the element that was most recently inserted is also the element that is removed first. A stack is a linear data structure that follows this rule. It employs the commands 'push' and 'pop' to add elements to the stack and, accordingly, remove the top element from the stack. The 'peek' technique additionally enables Access to the top element without removing it.</p> <p> <strong>Features of a stack:</strong> </p> <ol class="points"> <tr><td>LIFO behavior:</td> The last element pushed onto the stack is the first one to be popped out, making it suitable for applications where the order of insertion and removal is important. </tr><tr><td>Limited Access:</td> Stacks typically provide restricted Access to elements. You can only access the topmost element, and to reach other elements, you need to pop the elements above them. </tr><tr><td>Dynamic size:</td> Stacks can be implemented using arrays or linked lists, allowing for a dynamic size. They can grow or shrink as needed during runtime. </tr></ol> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Simplicity:</td> Stacks are easy to understand and implement. </tr><tr><td>Efficiency:</td> Insertion and deletion operations have a time complexity of O(1). </tr><tr><td>Function call management:</td> Stacks efficiently manage function calls and variable storage. </tr><tr><td>Undo/Redo functionality:</td> Stacks enable undo and redo operations in applications. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Limited Access:</td> Access to elements is restricted to the top of the stack. </tr><tr><td>Size restrictions:</td> Stacks may have size limitations depending on the implementation. </tr><tr><td>Not suitable for all scenarios:</td> Stacks are specific to LIFO behavior and may not be appropriate in other cases. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> StackExample.java</p> <pre> import java.util.Stack; public class StackExample { public static void main(String[] args) { // Create a stack Stack stack=new Stack(); // Push elements onto the stack stack.push(10); stack.push(20); stack.push(30); // Print the top element of the stack System.out.println('Top element:'+stack.peek()); // Pop elements from the stack int poppedElement=stack.pop(); System.out.println('Popped element:'+poppedElement); // Check if the stack is empty System.out.println('Is stack empty?'+stack.isEmpty()); // Get the size of the stack System.out.println('Stack size:'+stack.size()); // Iterate over the stack System.out.println('Stack elements:'); for (Integer element:stack) { System.out.println(element); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Top element:30 Popped element:30 Is stack empty?false Stack size:2 Stack elements: 10 20 </pre> <h3>5) Queue:</h3> <p>A queue is a linear data structure in Java that follows the First-In-First-Out (FIFO) principle. It represents a collection of elements where elements are inserted at the rear and removed from the front.</p> <p> <strong>Features:</strong> </p> <ol class="points"> <tr><td>Enqueue:</td> Adding an element to the rear of the queue. </tr><tr><td>Dequeue:</td> Removing an element from the front of the queue. </tr><tr><td>Peek:</td> Retrieve the element at the front of the queue without removing it. </tr><tr><td>Size:</td> Determining the number of elements in the queue. </tr><tr><td>Empty Check:</td> Checking if the queue is empty. </tr></ol> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>FIFO Behavior:</td> Elements are processed in the order of their insertion, ensuring the preservation of the original sequence. </tr><tr><td>Efficient Insertion and Removal:</td> Adding and removing elements from a queue is fast and has a constant time complexity of O(1). </tr><tr><td>Synchronization:</td> Java provides synchronized queue implementations, making them safe for concurrent programming. </tr><tr><td>Standardized Interface:</td> The Queue interface in Java offers a common set of methods, allowing easy interchangeability between different queue implementations. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>No Random Access:</td> Queues do not support direct Access to elements in the middle. Accessing specific positions requires dequeuing preceding elements. </tr><tr><td>Limited Size:</td> Some queue implementations have a fixed size or capacity, leading to overflow or exceptions when exceeding the maximum size. </tr><tr><td>Inefficient Search:</td> Searching for an element in a queue requires dequeuing until a match is found, resulting in a linear search with potentially high time complexity. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> QueueExample.java</p> <pre> import java.util.Stack; import java.util.LinkedList; import java.util.Queue; public class QueueExample { public static void main(String[] args) { // Create a Queue to store integers Queue queue=new LinkedList(); // Enqueue elements to the Queue queue.offer(10); queue.offer(20); queue.offer(30); queue.offer(40); queue.offer(50); //Access and print the front element of the Queue System.out.println('Front element:'+queue.peek()); // Dequeue elements from the Queue and print them while (!queue.isEmpty()) { int element=queue.poll(); System.out.println('Dequeued element:'+element); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Front element:10 Dequeued element:10 Dequeued element:20 Dequeued element:30 Dequeued element:40 Dequeued element:50 </pre> <h3>6) HashMap:</h3> <p>A HashMap is a data structure in Java that provides a way to store and retrieve key-value pairs. It is part of the Java Collections Framework and is implemented based on the hash table data structure.</p> <p> <strong>Functions:</strong> </p> <ul> <tr><td>put(key, value):</td> Inserts the specified key-value pair into the HashMap. </tr><tr><td>get(key):</td> Retrieves the value associated with the specified key. </tr><tr><td>containsKey(key):</td> Checks if the HashMap contains the specified key. </tr><tr><td>containsValue(value):</td> Checks if the HashMap contains the specified value. </tr><tr><td>remove(key):</td> Removes the key-value pair associated with the specified key from the HashMap. </tr><tr><td>size():</td> Returns the number of key-value pairs in the HashMap. </tr><tr><td>isEmpty():</td> Checks if the HashMap is empty. </tr><tr><td>keySet():</td> Returns a Set containing all the keys in the HashMap. </tr><tr><td>values():</td> Returns a Collection containing all the values in the HashMap. </tr><tr><td>clear():</td> Removes all the key-value pairs from the HashMap. </tr></ul> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Efficient Retrieval:</td> HashMap provides fast retrieval of values based on keys with constant-time complexity O(1). </tr><tr><td>Flexible Key-Value Pairing:</td> HashMap allows any non-null object as a key, enabling custom-defined keys for storing and retrieving data. </tr><tr><td>Dynamic Size:</td> HashMap can dynamically grow or shrink in size to handle varying amounts of data. </tr><tr><td>Compatibility with Java Collections Framework:</td> HashMap implements the Map interface, allowing seamless integration with other Collection classes. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Lack of Ordering:</td> HashMap does not preserve the order of elements. Use LinkedHashMap or TreeMap for specific ordering requirements. </tr><tr><td>Increased Memory Overhead:</td> HashMap requires additional memory for hash codes and internal structure compared to simpler data structures. </tr><tr><td>Slower Iteration:</td> Iterating over a HashMap can be slower compared to arrays or lists due to traversing the underlying hash table. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> HashMapExample.java</p> <pre> import java.util.HashMap; public class HashMapExample { public static void main(String[] args) { // Create a HashMap to store String keys and Integer values HashMap hashMap=new HashMap(); // Add key-value pairs to the HashMap hashMap.put('John',25); hashMap.put('Alice',30); hashMap.put('Bob',35); //Access and print values based on keys System.out.println('Age of John:'+hashMap.get('John')); System.out.println('Age of Alice:'+hashMap.get('Alice')); // Check if a key exists in the HashMap System.out.println('Is Bob present?'+hashMap.containsKey('Bob')); // Update the value associated with a key hashMap.put('Alice',32); // Remove a key-value pair from the HashMap hashMap.remove('John'); // Print all key-value pairs in the HashMap System.out.println('Key-Value pairs in the HashMap:'); for (String key : hashMap.keySet()) { System.out.println(key+':'+hashMap.get(key)); } // Check the size of the HashMap System.out.println('Size of the HashMap:'+hashMap.size()); } } </pre> <p> <strong>Output:</strong> </p> <pre> Age of John:25 Age of Alice:30 Is Bob present?true Key-Value pairs in the HashMap: Bob:35 Alice:32 Size of the HashMap:2 </pre> <h3>7) HashSet:</h3> <p>HashSet is a data structure in Java that implements the Set interface and stores elements in a hash table.</p> <p> <strong>Features:</strong> </p> <ol class="points"> <tr><td>Stores unique elements:</td> HashSet does not allow duplicate elements. Each element in the HashSet is unique. </tr><tr><td>Uses hash-based lookup:</td> HashSet uses the hash value of each element to determine its storage location, providing efficient element retrieval. </tr><tr><td>Unordered collection:</td> The elements in a HashSet are not stored in a specific order. The order of elements may change over time. </tr></ol> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Fast element lookup:</td> HashSet provides fast lookup operations, making it efficient to check if an element exists in the set. </tr><tr><td>No duplicate elements:</td> HashSet automatically handles duplicate elements and ensures that each element is unique. </tr><tr><td>Integration with Java Collections Framework:</td> HashSet implements the Set interface, making it compatible with other collection classes in the Java Collections Framework. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>No guaranteed order:</td> HashSet does not maintain the order of elements. If the order of elements is important, HashSet is not suitable. </tr><tr><td>No indexing:</td> HashSet does not provide direct indexing or positional Access to elements. To access elements, you need to iterate over the set. </tr><tr><td>Higher memory overhead:</td> HashSet requires additional memory to store hash values and maintain the hash table structure, resulting in higher memory usage compared to some other data structures. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> HashSetExample.java</p> <pre> import java.util.HashSet; public class HashSetExample { public static void main(String[] args) { // Create a HashSet HashSet set=new HashSet(); // Add elements to the HashSet set.add('Apple'); set.add('Banana'); set.add('Orange'); set.add('Grapes'); set.add('Mango'); // Print the HashSet System.out.println('HashSet:'+set); // Check if an element exists System.out.println('Contains 'Apple':'+set.contains('Apple')); // Remove an element set.remove('Banana'); // Print the updated HashSet System.out.println('Updated HashSet:'+set); // Get the size of the HashSet System.out.println('Size of HashSet:'+set.size()); // Clear the HashSet set.clear(); // Check if the HashSet is empty System.out.println('Is HashSet empty?'+set.isEmpty()); } } </pre> <p> <strong>Output:</strong> </p> <pre> HashSet:[Apple, Grapes, Mango, Orange, Banana] Contains 'Apple':true Updated HashSet:[Apple, Grapes, Mango, Orange] Size of HashSet:4 Is HashSet empty?true </pre> <h3>8) TreeSet:</h3> <p>TreeSet is an implementation of the SortedSet interface in Java that uses a self-balancing binary search tree called a red-black tree to store elements in sorted order.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Sorted Order:</td> TreeSet automatically maintains the elements in a sorted order based on their natural ordering or a custom comparator. It allows for efficient searching and retrieval of elements in ascending or descending order. </tr><tr><td>No Duplicate Elements:</td> TreeSet does not allow duplicate elements. It ensures that each element in the set is unique, which can be useful in scenarios where duplicate values should be avoided. </tr><tr><td>Efficient Operations:</td> TreeSet provides efficient operations like insertion, deletion, and searching. These operations have a time complexity of O(log n), where n is the number of elements in the set. </tr><tr><td>Navigable Set Operations:</td> TreeSet provides additional navigational methods, such as higher(), lower(), ceiling(), and floor(), which allow you to find elements that are greater than, less than, or equal to a given value. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Overhead:</td> TreeSet requires additional memory to store the internal data structure, which can lead to higher memory overhead compared to other set implementations. </tr><tr><td>Slower Insertion and Removal:</td> Insertion and removal operations in TreeSet involve maintaining the sorted order of elements, which may require tree restructuring. It can make these operations slightly slower compared to HashSet or LinkedHashSet. </tr><tr><td>Limited Customization:</td> TreeSet is primarily designed for natural ordering or a single custom comparator. It may need more flexibility for multiple sorting criteria or complex sorting logic. </tr></ol> <p> <strong>Functions:</strong> </p> <ul> <tr><td>add(element):</td> Adds an element to the TreeSet while maintaining the sorted order. </tr><tr><td>remove(element):</td> Removes the specified element from the TreeSet. </tr><tr><td>contains(element):</td> Checks if the TreeSet contains the specified element. </tr><tr><td>size():</td> Returns the number of elements in the TreeSet. </tr><tr><td>first():</td> Returns the first (lowest) element in the TreeSet. </tr><tr><td>last():</td> Returns the last (highest) element in the TreeSet. </tr><tr><td>higher(element):</td> Returns the least element in the TreeSet that is strictly greater than the given element. </tr><tr><td>lower(element):</td> Returns the greatest element in the TreeSet that is strictly less than the given element. </tr></ul> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> TreeSetExample.java</p> <pre> import java.util.TreeSet; public class TreeSetExample { public static void main(String[] args) { // Create a TreeSet TreeSet numbers=new TreeSet(); // Add elements to the TreeSet numbers.add(5); numbers.add(2); numbers.add(8); numbers.add(1); numbers.add(4); // Print the TreeSet System.out.println('Elements in the TreeSet:'+numbers); // Check if an element exists System.out.println('Does TreeSet contain 4?'+numbers.contains(4)); // Remove an element numbers.remove(2); // Print the TreeSet after removal System.out.println('Elements in the TreeSet after removal:'+numbers); // Get the size of the TreeSet System.out.println('Size of the TreeSet:'+numbers.size()); // Get the first and last element System.out.println('First element:'+numbers.first()); System.out.println('Last element:'+numbers.last()); // Iterate over the TreeSet System.out.println('Iterating over the TreeSet:'); for (int number:numbers) { System.out.println(number); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Elements in the TreeSet:[1, 2, 4, 5, 8] Does TreeSet contain 4? true Elements in the TreeSet after removal:[1, 4, 5, 8] Size of the TreeSet:4First element:1 Last element:8 Iterating over the TreeSet: 1 4 5 8 </pre> <h3>9) TreeMap:</h3> <p>TreeMap is a class in Java that implements the Map interface and provides a sorted key-value mapping based on the natural order of the keys or a custom comparator.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Sorted Ordering:</td> TreeMap maintains the keys in sorted order, which allows for efficient searching, retrieval, and range-based operations. </tr><tr><td>Key-Value Mapping:</td> TreeMap stores key-value pairs, enabling efficient lookup and retrieval of values based on the associated keys. </tr><tr><td>Red-Black Tree Implementation:</td> TreeMap uses a balanced binary search tree (Red-Black Tree) internally, ensuring efficient performance even for large datasets. </tr><tr><td>Support for Custom Comparators:</td> TreeMap allows the use of custom comparators to define the sorting order of the keys, providing flexibility in sorting criteria. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Memory Overhead:</td> TreeMap requires additional memory to store the internal tree structure and associated objects, resulting in higher memory usage compared to simpler data structures like HashMap. </tr><tr><td>Slower Insertion and Deletion:</td> Insertion and deletion operations in TreeMap have a time complexity of O(log n) due to the need for tree restructuring, making them slower compared to HashMap or LinkedHashMap. </tr><tr><td>Limited Performance for Unsorted Data:</td> TreeMap performs efficiently for sorted data, but its performance can degrade when dealing with unsorted data or frequent modifications, as it requires maintaining the sorted order. </tr></ol> <p> <strong>Functions:</strong> </p> <ul> <tr><td>put(key, value):</td> Inserts a key-value pair into the TreeMap. </tr><tr><td>get(key):</td> Retrieves the value associated with the specified key. </tr><tr><td>containsKey(key):</td> Checks if the TreeMap contains a specific key. </tr><tr><td>remove(key):</td> Removes the key-value pair associated with the specified key. </tr><tr><td>size():</td> Returns the number of key-value pairs in the TreeMap. </tr><tr><td>keySet():</td> Returns a set of all keys in the TreeMap. </tr><tr><td>values():</td> Returns a collection of all values in the TreeMap. </tr><tr><td>entrySet():</td> Returns a set of key-value pairs in the TreeMap. </tr></ul> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> TreeMapExample.java</p> <pre> import java.util.TreeMap; public class TreeMapExample { public static void main(String[] args) { // Create a TreeMap TreeMap scores=new TreeMap(); // Insert key-value pairs into the TreeMap scores.put('Alice',90); scores.put('Bob',80); scores.put('Charlie',95); scores.put('David',87); scores.put('Eve',92); //Access and print values from the TreeMap System.out.println('Score of Alice:'+scores.get('Alice')); System.out.println('Score of Charlie:'+scores.get('Charlie')); System.out.println('Score of David:'+scores.get('David')); // Update a value in the TreeMap scores.put('Bob',85); // Remove a key-value pair from the TreeMap scores.remove('Eve'); // Iterate through the TreeMap using a for-each loop System.out.println('Scores in the TreeMap:'); for (String name:scores.keySet()) { int score=scores.get(name); System.out.println(name+':'+score); } } } </pre> <p> <strong>Output:</strong> </p> <pre> Score of Alice:90 Score of Charlie:95 Score of David:87 Scores in the TreeMap: Alice:90 Bob:85 Charlie:95 David:87 </pre> <h3>10) Graph:</h3> <p>Graphs are data structure that represents a collection of interconnected nodes or vertices. They are composed of vertices and edges, where vertices represent entities and edges represent the relationships between those entities.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Versatility:</td> Graphs can represent a wide range of real-world scenarios, making them suitable for various applications such as social networks, transportation systems, and computer networks. </tr><tr><td>Relationship Representation:</td> Graphs provide a natural way to represent relationships and connections between entities, allowing for efficient analysis and traversal of these relationships. </tr><tr><td>Efficient Search and Traversal:</td> Graph algorithms like breadth-first search (BFS) and depth-first search (DFS) enable efficient traversal and searching of the graph's vertices and edges. </tr><tr><td>Modeling Complex Relationships:</td> Graphs can model complex relationships, including hierarchical structures, cyclic dependencies, and multiple connections between entities. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>Space Complexity:</td> Graphs can consume a significant amount of memory, especially large-scale graphs with many vertices and edges. </tr><tr><td>The complexity of Operations:</td> Certain graph operations, such as finding the shortest path or detecting cycles, can have high time complexity, particularly in dense graphs. </tr><tr><td>Difficulty in Maintenance:</td> Modifying or updating a graph can be complex, as changes in the graph's structure may impact its connectivity and existing algorithms. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> GraphExample.java</p> <pre> import java.util.*; public class GraphExample { private int V; // Number of vertices private List<list> adjacencyList; // Adjacency list representation public GraphExample(int V) { this.V=V; adjacencyList=new ArrayList(V); // Initialize the adjacency list for (int i=0;i<v;i++) { adjacencylist.add(new arraylist()); } function to add an edge between two vertices public void addedge(int source,int destination) adjacencylist.get(source).add(destination); adjacencylist.get(destination).add(source); perform breadth-first search traversal of the graph bfs(int startvertex) boolean[] visited="new" boolean[v]; queue linkedlist(); visited[startvertex]="true;" queue.add(startvertex); while (!queue.isempty()) int currentvertex="queue.poll();" system.out.print(currentvertex+' '); list neighbors="adjacencyList.get(currentVertex);" for (int neighbor:neighbors) if (!visited[neighbor]) visited[neighbor]="true;" queue.add(neighbor); system.out.println(); depth-first dfs(int dfsutil(startvertex,visited); private dfsutil(int vertex,boolean[] visited) visited[vertex]="true;" system.out.print(vertex+' dfsutil(neighbor,visited); static main(string[] args) v="5;" number graphexample graphexample(v); edges graph.addedge(0,1); graph.addedge(0,2); graph.addedge(1,3); graph.addedge(2,3); graph.addedge(2,4); system.out.print('bfs traversal: graph.bfs(0); system.out.print('dfs graph.dfs(0); < pre> <p> <strong>Output:</strong> </p> <pre> BFS traversal: 0 1 2 3 4 DFS traversal: 0 1 3 2 4 </pre> <h3>11) Tree:</h3> <p>A tree is a widely used data structure in computer science that represents a hierarchical structure. It consists of nodes connected by edges, where each node can have zero or more child nodes.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Hierarchical Structure:</td> Trees provide a natural way to represent hierarchical relationships, such as file systems, organization charts, or HTML/XML documents. </tr><tr><td>Efficient Search:</td> Binary search trees enable efficient searching with a time complexity of O(log n), making them suitable for storing and retrieving sorted data. </tr><tr><td>Fast Insertion and Deletion:</td> Tree data structures offer efficient insertion and deletion operations, especially when balanced, such as AVL trees or Red-Black trees. </tr><tr><td>Ordered Iteration:</td> In-order traversal of a binary search tree gives elements in a sorted order, which is helpful for tasks like printing elements in sorted order or finding the next/previous element. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>High Memory Overhead:</td> Trees require additional memory to store node references or pointers, which can result in higher memory usage compared to linear data structures like arrays or lists. </tr><tr><td>Complex Implementation:</td> Implementing and maintaining a tree data structure can be more complex compared to other data structures like arrays or lists, especially for balanced tree variants. </tr><tr><td>Limited Operations:</td> Some tree variants, like binary search trees, do not support efficient operations like finding the kth smallest element or finding the rank of an element. </tr></ol> <p> <strong>Functions:</strong> </p> <ol class="points"> <tr><td>Insertion:</td> Add a new node to the tree. </tr><tr><td>Deletion:</td> Remove a node from the tree. </tr><tr><td>Search:</td> Find a specific node or element in the tree. </tr><tr><td>Traversal:</td> Traverse the tree in different orders, such as in-order, pre-order, or post-order. </tr><tr><td>Height/Depth:</td> Calculate the height or depth of the tree. </tr><tr><td>Balance:</td> Ensure the tree remains balanced to maintain efficient operations. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> TreeExample.java</p> <pre> import java.util.*; class TreeNode { int value; TreeNode left; TreeNode right; public TreeNode(int value) { this.value = value; left = null; right = null; } } public class TreeExample { public static void main(String[] args) { // Create a binary search tree TreeNode root = new TreeNode(50); root.left = new TreeNode(30); root.right = new TreeNode(70); root.left.left = new TreeNode(20); root.left.right = new TreeNode(40); root.right.left = new TreeNode(60); root.right.right = new TreeNode(80); // Perform common operations System.out.println('In-order Traversal:'); inOrderTraversal(root); System.out.println('
Search for value 40: '+search(root, 40)); System.out.println('Search for value 90: '+search(root, 90)); int minValue = findMinValue(root); System.out.println('Minimum value in the tree: '+minValue); int maxValue = findMaxValue(root); System.out.println('Maximum value in the tree: '+maxValue); } // In-order traversal: left subtree, root, right subtree public static void inOrderTraversal(TreeNode node) { if (node != null) { inOrderTraversal(node.left); System.out.print(node.value + ' '); inOrderTraversal(node.right); } } // Search for a value in the tree public static boolean search(TreeNode node, int value) { if (node == null) return false; if (node.value == value) return true; if (value <node.value) return search(node.left, value); else search(node.right, } find the minimum value in tree public static int findminvalue(treenode node) { if (node.left="=" null) node.value; findminvalue(node.left); maximum findmaxvalue(treenode (node.right="=" findmaxvalue(node.right); < pre> <p> <strong>Output:</strong> </p> <pre> In-order Traversal:20 30 40 50 60 70 80 Search for value 40: true Search for value 90: false Minimum value in the tree: 20 Maximum value in the tree: 80 </pre> <hr></node.value)></pre></v;i++)></list></pre></numbers.length;i++)> 2) Lista de matrices:
ArrayList en Java es una estructura de datos dinámica que permite el almacenamiento y manipulación de elementos. Es parte del marco de colecciones de Java y se implementa utilizando una matriz internamente.
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Implementación:
Nombre del archivo: ArrayListExample.java
import java.util.*; public class ArrayListExample { public static void main(String[] args) { // Create an ArrayList to store integers ArrayList numbers=new ArrayList(List.of(10,20,30,40,50)); //Access and print elements from the ArrayList System.out.println('Element at index 0:'+numbers.get(0)); System.out.println('Element at index 2:'+numbers.get(2)); System.out.println('Element at index 4:'+numbers.get(4)); // Calculate and print the sum of all elements in the ArrayList int sum=numbers.stream().mapToInt(Integer::intValue).sum(); System.out.println('Sum of ArrayList elements:'+sum); // Update an element in the ArrayList numbers.set(2,35); System.out.println('Updated element at index 2:'+numbers.get(2)); // Iterate through the ArrayList using a for-each loop and print the elements System.out.println('Elements in the ArrayList:'); for (int number:numbers) { System.out.println(number); } } } Producción:
Element at index 0:10 Element at index 2:30 Element at index 4:50 Sum of ArrayList elements:150 Updated element at index 2:35 Elements in the ArrayList: 10 20 35 40 50
3) Lista enlazada:
Una lista enlazada es una estructura de datos lineal en la que los elementos se almacenan en objetos separados llamados nodos. En el elemento de datos de cada nodo se incluye un enlace de referencia al siguiente nodo de la secuencia. El nodo final de la lista se vincula a nulo, lo que indica que la lista ha finalizado.
A diferencia de las matrices, las listas vinculadas no requieren una asignación de memoria contigua. Cada nodo en una lista vinculada se puede asignar de forma independiente, lo que permite la asignación dinámica de memoria y operaciones eficientes de inserción y eliminación.
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Implementación:
Nombre del archivo: LinkedList1.java
import java.util.*; public class LinkedList1 { public static void main(String[] args) { // Create a LinkedList to store integers LinkedList linkedList1 = new LinkedList(); // Add elements to the LinkedList linkedList1.add(10); linkedList1.add(20); linkedList1.add(30); linkedList1.add(40); linkedList1.add(50); // Print the LinkedList System.out.println('LinkedList:'+linkedList1); // Remove an element from the LinkedList linkedList1.removeFirst(); System.out.println('LinkedList after removing first element:'+linkedList1); // Check if an element exists in the LinkedList boolean containsElement=linkedList1.contains(30); System.out.println('LinkedList contains element 30?'+containsElement); // Get the first and last elements of the LinkedList int firstElement=linkedList1.getFirst(); int lastElement=linkedList1.getLast(); System.out.println('First element:'+firstElement); System.out.println('Last element:'+lastElement); // Clear the LinkedList linkedList1.clear(); System.out.println('LinkedList after clearing:'+linkedList1); } } Producción:
LinkedList:[10, 20, 30, 40, 50] LinkedList after removing first element:[20, 30, 40, 50] LinkedList contains element 30?true First element:20 Last element:50 LinkedList after clearing:[]
4) Pila:
El principio de último en entrar, primero en salir (LIFO) dicta que el elemento que se insertó más recientemente es también el elemento que se elimina primero. Una pila es una estructura de datos lineal que sigue esta regla. Emplea los comandos 'push' y 'pop' para agregar elementos a la pila y, en consecuencia, eliminar el elemento superior de la pila. La técnica 'peek' permite además acceder al elemento superior sin quitarlo.
Características de una pila:
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Implementación:
Nombre del archivo: StackExample.java
import java.util.Stack; public class StackExample { public static void main(String[] args) { // Create a stack Stack stack=new Stack(); // Push elements onto the stack stack.push(10); stack.push(20); stack.push(30); // Print the top element of the stack System.out.println('Top element:'+stack.peek()); // Pop elements from the stack int poppedElement=stack.pop(); System.out.println('Popped element:'+poppedElement); // Check if the stack is empty System.out.println('Is stack empty?'+stack.isEmpty()); // Get the size of the stack System.out.println('Stack size:'+stack.size()); // Iterate over the stack System.out.println('Stack elements:'); for (Integer element:stack) { System.out.println(element); } } } Producción:
Top element:30 Popped element:30 Is stack empty?false Stack size:2 Stack elements: 10 20
5) Cola:
Una cola es una estructura de datos lineal en Java que sigue el principio de primero en entrar, primero en salir (FIFO). Representa una colección de elementos donde los elementos se insertan en la parte trasera y se retiran del frente.
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Implementación:
Nombre del archivo: QueueExample.java
import java.util.Stack; import java.util.LinkedList; import java.util.Queue; public class QueueExample { public static void main(String[] args) { // Create a Queue to store integers Queue queue=new LinkedList(); // Enqueue elements to the Queue queue.offer(10); queue.offer(20); queue.offer(30); queue.offer(40); queue.offer(50); //Access and print the front element of the Queue System.out.println('Front element:'+queue.peek()); // Dequeue elements from the Queue and print them while (!queue.isEmpty()) { int element=queue.poll(); System.out.println('Dequeued element:'+element); } } } Producción:
Front element:10 Dequeued element:10 Dequeued element:20 Dequeued element:30 Dequeued element:40 Dequeued element:50
6) Mapa Hash:
Un HashMap es una estructura de datos en Java que proporciona una forma de almacenar y recuperar pares clave-valor. Es parte del marco de colecciones de Java y se implementa en función de la estructura de datos de la tabla hash.
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Nombre del archivo: HashMapExample.java
import java.util.HashMap; public class HashMapExample { public static void main(String[] args) { // Create a HashMap to store String keys and Integer values HashMap hashMap=new HashMap(); // Add key-value pairs to the HashMap hashMap.put('John',25); hashMap.put('Alice',30); hashMap.put('Bob',35); //Access and print values based on keys System.out.println('Age of John:'+hashMap.get('John')); System.out.println('Age of Alice:'+hashMap.get('Alice')); // Check if a key exists in the HashMap System.out.println('Is Bob present?'+hashMap.containsKey('Bob')); // Update the value associated with a key hashMap.put('Alice',32); // Remove a key-value pair from the HashMap hashMap.remove('John'); // Print all key-value pairs in the HashMap System.out.println('Key-Value pairs in the HashMap:'); for (String key : hashMap.keySet()) { System.out.println(key+':'+hashMap.get(key)); } // Check the size of the HashMap System.out.println('Size of the HashMap:'+hashMap.size()); } } Producción:
Age of John:25 Age of Alice:30 Is Bob present?true Key-Value pairs in the HashMap: Bob:35 Alice:32 Size of the HashMap:2
7) Conjunto de hash:
HashSet es una estructura de datos en Java que implementa la interfaz Set y almacena elementos en una tabla hash.
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Implementación:
Nombre del archivo: HashSetExample.java
import java.util.HashSet; public class HashSetExample { public static void main(String[] args) { // Create a HashSet HashSet set=new HashSet(); // Add elements to the HashSet set.add('Apple'); set.add('Banana'); set.add('Orange'); set.add('Grapes'); set.add('Mango'); // Print the HashSet System.out.println('HashSet:'+set); // Check if an element exists System.out.println('Contains 'Apple':'+set.contains('Apple')); // Remove an element set.remove('Banana'); // Print the updated HashSet System.out.println('Updated HashSet:'+set); // Get the size of the HashSet System.out.println('Size of HashSet:'+set.size()); // Clear the HashSet set.clear(); // Check if the HashSet is empty System.out.println('Is HashSet empty?'+set.isEmpty()); } } Producción:
HashSet:[Apple, Grapes, Mango, Orange, Banana] Contains 'Apple':true Updated HashSet:[Apple, Grapes, Mango, Orange] Size of HashSet:4 Is HashSet empty?true
8) Conjunto de árboles:
TreeSet es una implementación de la interfaz SortedSet en Java que utiliza un árbol de búsqueda binario autoequilibrado llamado árbol rojo-negro para almacenar elementos en orden.
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Nombre del archivo: TreeSetExample.java
import java.util.TreeSet; public class TreeSetExample { public static void main(String[] args) { // Create a TreeSet TreeSet numbers=new TreeSet(); // Add elements to the TreeSet numbers.add(5); numbers.add(2); numbers.add(8); numbers.add(1); numbers.add(4); // Print the TreeSet System.out.println('Elements in the TreeSet:'+numbers); // Check if an element exists System.out.println('Does TreeSet contain 4?'+numbers.contains(4)); // Remove an element numbers.remove(2); // Print the TreeSet after removal System.out.println('Elements in the TreeSet after removal:'+numbers); // Get the size of the TreeSet System.out.println('Size of the TreeSet:'+numbers.size()); // Get the first and last element System.out.println('First element:'+numbers.first()); System.out.println('Last element:'+numbers.last()); // Iterate over the TreeSet System.out.println('Iterating over the TreeSet:'); for (int number:numbers) { System.out.println(number); } } } Producción:
Elements in the TreeSet:[1, 2, 4, 5, 8] Does TreeSet contain 4? true Elements in the TreeSet after removal:[1, 4, 5, 8] Size of the TreeSet:4First element:1 Last element:8 Iterating over the TreeSet: 1 4 5 8
9) Mapa de árbol:
TreeMap es una clase en Java que implementa la interfaz Map y proporciona un mapeo clave-valor ordenado basado en el orden natural de las claves o un comparador personalizado.
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Nombre del archivo: TreeMapExample.java
import java.util.TreeMap; public class TreeMapExample { public static void main(String[] args) { // Create a TreeMap TreeMap scores=new TreeMap(); // Insert key-value pairs into the TreeMap scores.put('Alice',90); scores.put('Bob',80); scores.put('Charlie',95); scores.put('David',87); scores.put('Eve',92); //Access and print values from the TreeMap System.out.println('Score of Alice:'+scores.get('Alice')); System.out.println('Score of Charlie:'+scores.get('Charlie')); System.out.println('Score of David:'+scores.get('David')); // Update a value in the TreeMap scores.put('Bob',85); // Remove a key-value pair from the TreeMap scores.remove('Eve'); // Iterate through the TreeMap using a for-each loop System.out.println('Scores in the TreeMap:'); for (String name:scores.keySet()) { int score=scores.get(name); System.out.println(name+':'+score); } } } Producción:
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Score of Alice:90 Score of Charlie:95 Score of David:87 Scores in the TreeMap: Alice:90 Bob:85 Charlie:95 David:87
10) Gráfico:
Los gráficos son una estructura de datos que representa una colección de nodos o vértices interconectados. Están compuestos de vértices y aristas, donde los vértices representan entidades y las aristas representan las relaciones entre esas entidades.
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Implementación:
Nombre del archivo: GraphExample.java
import java.util.*; public class GraphExample { private int V; // Number of vertices private List<list> adjacencyList; // Adjacency list representation public GraphExample(int V) { this.V=V; adjacencyList=new ArrayList(V); // Initialize the adjacency list for (int i=0;i<v;i++) { adjacencylist.add(new arraylist()); } function to add an edge between two vertices public void addedge(int source,int destination) adjacencylist.get(source).add(destination); adjacencylist.get(destination).add(source); perform breadth-first search traversal of the graph bfs(int startvertex) boolean[] visited="new" boolean[v]; queue linkedlist(); visited[startvertex]="true;" queue.add(startvertex); while (!queue.isempty()) int currentvertex="queue.poll();" system.out.print(currentvertex+\' \'); list neighbors="adjacencyList.get(currentVertex);" for (int neighbor:neighbors) if (!visited[neighbor]) visited[neighbor]="true;" queue.add(neighbor); system.out.println(); depth-first dfs(int dfsutil(startvertex,visited); private dfsutil(int vertex,boolean[] visited) visited[vertex]="true;" system.out.print(vertex+\' dfsutil(neighbor,visited); static main(string[] args) v="5;" number graphexample graphexample(v); edges graph.addedge(0,1); graph.addedge(0,2); graph.addedge(1,3); graph.addedge(2,3); graph.addedge(2,4); system.out.print(\'bfs traversal: graph.bfs(0); system.out.print(\'dfs graph.dfs(0); < pre> <p> <strong>Output:</strong> </p> <pre> BFS traversal: 0 1 2 3 4 DFS traversal: 0 1 3 2 4 </pre> <h3>11) Tree:</h3> <p>A tree is a widely used data structure in computer science that represents a hierarchical structure. It consists of nodes connected by edges, where each node can have zero or more child nodes.</p> <p> <strong>Advantages:</strong> </p> <ol class="points"> <tr><td>Hierarchical Structure:</td> Trees provide a natural way to represent hierarchical relationships, such as file systems, organization charts, or HTML/XML documents. </tr><tr><td>Efficient Search:</td> Binary search trees enable efficient searching with a time complexity of O(log n), making them suitable for storing and retrieving sorted data. </tr><tr><td>Fast Insertion and Deletion:</td> Tree data structures offer efficient insertion and deletion operations, especially when balanced, such as AVL trees or Red-Black trees. </tr><tr><td>Ordered Iteration:</td> In-order traversal of a binary search tree gives elements in a sorted order, which is helpful for tasks like printing elements in sorted order or finding the next/previous element. </tr></ol> <p> <strong>Disadvantages:</strong> </p> <ol class="points"> <tr><td>High Memory Overhead:</td> Trees require additional memory to store node references or pointers, which can result in higher memory usage compared to linear data structures like arrays or lists. </tr><tr><td>Complex Implementation:</td> Implementing and maintaining a tree data structure can be more complex compared to other data structures like arrays or lists, especially for balanced tree variants. </tr><tr><td>Limited Operations:</td> Some tree variants, like binary search trees, do not support efficient operations like finding the kth smallest element or finding the rank of an element. </tr></ol> <p> <strong>Functions:</strong> </p> <ol class="points"> <tr><td>Insertion:</td> Add a new node to the tree. </tr><tr><td>Deletion:</td> Remove a node from the tree. </tr><tr><td>Search:</td> Find a specific node or element in the tree. </tr><tr><td>Traversal:</td> Traverse the tree in different orders, such as in-order, pre-order, or post-order. </tr><tr><td>Height/Depth:</td> Calculate the height or depth of the tree. </tr><tr><td>Balance:</td> Ensure the tree remains balanced to maintain efficient operations. </tr></ol> <p> <strong>Implementation:</strong> </p> <p> <strong>FileName:</strong> TreeExample.java</p> <pre> import java.util.*; class TreeNode { int value; TreeNode left; TreeNode right; public TreeNode(int value) { this.value = value; left = null; right = null; } } public class TreeExample { public static void main(String[] args) { // Create a binary search tree TreeNode root = new TreeNode(50); root.left = new TreeNode(30); root.right = new TreeNode(70); root.left.left = new TreeNode(20); root.left.right = new TreeNode(40); root.right.left = new TreeNode(60); root.right.right = new TreeNode(80); // Perform common operations System.out.println('In-order Traversal:'); inOrderTraversal(root); System.out.println('
Search for value 40: '+search(root, 40)); System.out.println('Search for value 90: '+search(root, 90)); int minValue = findMinValue(root); System.out.println('Minimum value in the tree: '+minValue); int maxValue = findMaxValue(root); System.out.println('Maximum value in the tree: '+maxValue); } // In-order traversal: left subtree, root, right subtree public static void inOrderTraversal(TreeNode node) { if (node != null) { inOrderTraversal(node.left); System.out.print(node.value + ' '); inOrderTraversal(node.right); } } // Search for a value in the tree public static boolean search(TreeNode node, int value) { if (node == null) return false; if (node.value == value) return true; if (value <node.value) return search(node.left, value); else search(node.right, } find the minimum value in tree public static int findminvalue(treenode node) { if (node.left="=" null) node.value; findminvalue(node.left); maximum findmaxvalue(treenode (node.right="=" findmaxvalue(node.right); < pre> <p> <strong>Output:</strong> </p> <pre> In-order Traversal:20 30 40 50 60 70 80 Search for value 40: true Search for value 90: false Minimum value in the tree: 20 Maximum value in the tree: 80 </pre> <hr></node.value)></pre></v;i++)></list> 11) Árbol:
Un árbol es una estructura de datos ampliamente utilizada en informática que representa una estructura jerárquica. Consta de nodos conectados por bordes, donde cada nodo puede tener cero o más nodos secundarios.
Ventajas:
Desventajas:
Funciones:
Implementación:
Nombre del archivo: TreeExample.java
import java.util.*; class TreeNode { int value; TreeNode left; TreeNode right; public TreeNode(int value) { this.value = value; left = null; right = null; } } public class TreeExample { public static void main(String[] args) { // Create a binary search tree TreeNode root = new TreeNode(50); root.left = new TreeNode(30); root.right = new TreeNode(70); root.left.left = new TreeNode(20); root.left.right = new TreeNode(40); root.right.left = new TreeNode(60); root.right.right = new TreeNode(80); // Perform common operations System.out.println('In-order Traversal:'); inOrderTraversal(root); System.out.println('
Search for value 40: '+search(root, 40)); System.out.println('Search for value 90: '+search(root, 90)); int minValue = findMinValue(root); System.out.println('Minimum value in the tree: '+minValue); int maxValue = findMaxValue(root); System.out.println('Maximum value in the tree: '+maxValue); } // In-order traversal: left subtree, root, right subtree public static void inOrderTraversal(TreeNode node) { if (node != null) { inOrderTraversal(node.left); System.out.print(node.value + ' '); inOrderTraversal(node.right); } } // Search for a value in the tree public static boolean search(TreeNode node, int value) { if (node == null) return false; if (node.value == value) return true; if (value <node.value) return search(node.left, value); else search(node.right, } find the minimum value in tree public static int findminvalue(treenode node) { if (node.left="=" null) node.value; findminvalue(node.left); maximum findmaxvalue(treenode (node.right="=" findmaxvalue(node.right); < pre> <p> <strong>Output:</strong> </p> <pre> In-order Traversal:20 30 40 50 60 70 80 Search for value 40: true Search for value 90: false Minimum value in the tree: 20 Maximum value in the tree: 80 </pre> <hr></node.value)>