MATLAB Source Files for Pattern Recognition and Clustering Algorithms

Pattern recognition and cluster analysis are fundamental techniques in machine learning and data mining, widely applied in image processing, bioinformatics, market analysis, and other domains. This collection of clustering algorithm source files implemented in MATLAB integrates multiple classical methods, providing researchers and engineers with a convenient toolkit for data analysis.

The following are common clustering methods included:

K-means Clustering: Partitions data points into K clusters through iterative optimization, where each cluster's centroid best represents its members. MATLAB's built-in `kmeans` function can be directly invoked, making it suitable for handling large-scale numerical datasets. The implementation typically requires specifying the number of clusters and uses Euclidean distance for convergence.

Hierarchical Clustering: Builds a tree-like structure (Dendrogram) by progressively merging or splitting clusters based on a distance matrix. MATLAB's `linkage` function supports different connectivity methods (single, complete, average linkage), while `cluster` generates the final groupings. The dendrogram visualization helps determine optimal cluster cuts.

DBSCAN: A density-based algorithm capable of identifying arbitrarily shaped clusters while filtering noise points. While requiring manual implementation or toolbox integration, its core parameters include neighborhood radius (`eps`) and minimum points (`minPts`). The algorithm excels at discovering non-spherical clusters without predefining cluster counts.

Gaussian Mixture Models (GMM): Assumes data is generated from a mixture of multiple Gaussian distributions, with parameters estimated through Expectation-Maximization (EM) algorithm. MATLAB's `fitgmdist` function implements this method, particularly suitable for probabilistic soft clustering scenarios where data points can belong to multiple clusters.

Self-Organizing Maps (SOM): A neural network approach that maps high-dimensional data to low-dimensional grids while preserving topological relationships. Implementation requires either the Neural Network Toolbox or custom code, with the `selforgmap` function facilitating creation and training of SOM networks.

Extension Considerations: Performance Optimization: For large datasets, consider integrating parallel computing (`parfor` loops) or approximate algorithms to accelerate processing. Evaluation Metrics: Quantify clustering quality using silhouette coefficients (`silhouette` function) or Calinski-Harabasz index to validate cluster separation and cohesion. Visualization: Utilize MATLAB's `scatter`, `plot` functions, and 3D graphing tools to intuitively display clustering results, with color-coding for different clusters and marker styles for outliers.

These methods cover diverse requirements from simple partitioning to complex probabilistic modeling, enabling users to select appropriate algorithms based on data characteristics and application scenarios.