Explore MATLAB source code curated for "聚类算法" with clean implementations, documentation, and examples.
Application Context: This algorithm implements k-means image segmentation and clustering technology. Key Technology: A clustering technique that segments images into 3 clusters using MATLAB tools, featuring centroid initialization and iterative optimization for pixel classification.
This project provides source code and examples for four clustering algorithms, aiming to develop a standardized and extensible toolkit for clustering tasks. The implementation includes: 1. Clustering algorithms: K-means, K-medoids, FCMclust, GKclust, and GGclust 2. Cluster visualization: 2D plotting capabilities for displaying clustering results 3. Validation metrics: Comprehensive evaluation mechanisms calculating Partition Coefficient (PC), Classification Entropy (CE), Partition Index (SC), Separation Index (S), Xie and Beni's Index (XB), Dunn's Index (DI), and Alternative Dunn Index (DII)
MATLAB clustering algorithm implementation featuring absolute distance, Chebyshev distance, and mean distance metrics. Includes a GUI interface with detailed documentation for practical usage and algorithm analysis.
A newly developed MATLAB source code implementation of the K-means clustering algorithm, specifically designed for handling multidimensional datasets with enhanced optimization features.
This repository contains nearly all major clustering algorithms implemented in MATLAB, featuring executable demo examples that demonstrate practical applications and algorithm performance.
Implementation of the relatively new Dominant-Set clustering algorithm, including both core clustering code and test scripts. This graph theory-based approach offers significantly lower computational complexity compared to Normalized Cut while automatically determining optimal cluster numbers.
Implementation of color image segmentation using MATLAB, primarily based on clustering algorithms with code-level explanations of key functions and implementation approaches.
While traditional genetic algorithms exhibit significant individual diversity during early iterations, the classic roulette wheel selection mechanism causes offspring production to correlate directly with parental fitness values. This often leads to premature convergence as superior individuals dominate the population prematurely. During later stages, fitness values tend to converge, diminishing the reproductive advantages of elite individuals and stalling evolutionary progress. The algorithm incorporates fitness scaling where temperature-controlled annealing maintains balanced selection pressure during high-temperature phases (early iterations), while intensified scaling at lower temperatures amplifies fitness differences to accentuate elite advantages. This hybrid approach leverages complementary strengths of simulated annealing and genetic algorithms to overcome premature convergence, with customized genetic encoding and fitness functions specifically designed for clustering problems to ensure efficient global convergence.
This MATLAB code implements the k-means clustering algorithm with complete functionality including main function and sample data. The program performs binary classification (k=2 clusters) and is ready to run immediately without additional setup. The implementation includes core k-means operations such as centroid initialization, distance calculation, cluster assignment, and centroid updating through iterative optimization.
Source code implementation of the Affinity Propagation clustering algorithm from the 2007 Science journal paper "Clustering by passing messages between data points" - a message-passing based clustering approach.