Explore MATLAB source code curated for "k均值算法" with clean implementations, documentation, and examples.
Implementation of k-means algorithm for dynamic clustering using MATLAB with graphical analysis of classification results, exploring algorithm performance and optimization approaches.
A ready-to-run MATLAB program for k-means clustering algorithm with evaluation metrics and visualization tools.
A MATLAB implementation of the K-means algorithm tested on Iris dataset, featuring robust debugging and stable experimental results with detailed performance analysis.
Multiple MATLAB implementations of forward cloud generator and k-means clustering algorithm, all thoroughly debugged and ready for use with detailed code explanations.
Self-implemented pattern recognition algorithms including Perceptron Algorithm, Multi-class Perceptron Algorithm, and K-means Clustering with detailed code implementation
Implementation of K-Means Clustering Algorithm: Given K number of clusters, the algorithm partitions n objects into K classes, maximizing within-cluster similarity while minimizing between-cluster similarity. The implementation involves iterative centroid updates and distance calculations using MATLAB's vectorized operations for efficient clustering.
Implementation of K-Means Clustering Algorithm: This algorithm partitions n objects into K clusters based on maximizing intra-cluster similarity while minimizing inter-cluster similarity. Limitations include potentially uneven cluster sizes and sensitivity to noisy data. Enhanced approach: k-medoids method selects representative objects (medoids) instead of centroids to define clusters. Implementation steps: 1) Randomly initialize K medoids; 2) Assign remaining objects to nearest medoids; 3) Iteratively optimize medoid selection by minimizing replacement cost. Code implementation typically involves distance calculations, cluster assignment loops, and convergence checks.
MATLAB implementation of K-means clustering algorithm for face recognition applications, featuring practical code examples and algorithm explanations.
K-means clustering algorithm implemented in MATLAB - performs data classification by accepting the number of clusters as input parameter with detailed code explanation
MATLAB code implementation of the K-means clustering algorithm featuring data preprocessing, centroid optimization, and performance evaluation using the Iris dataset