Explore MATLAB source code curated for "聚类" with clean implementations, documentation, and examples.
My comprehensive pattern recognition project includes implementations of Bayes classification, Fisher linear discriminant analysis, PCA and LDA feature extraction, K-means clustering, and Parzen window algorithm. The code is thoroughly commented and comes with test datasets for validation.
K-means algorithm image segmentation example (clustering image pixels into three categories) with implementation insights
Three MATLAB implementations of Radial Basis Function (RBF) neural networks featuring distinct training methodologies: cluster-based RBF, gradient descent-based RBF, and Orthogonal Least Squares (OLS)-based RBF networks with detailed algorithmic descriptions and code structure explanations.
This clustering implementation using MATLAB delivers excellent results and includes sample data that can be executed directly with a single click
The Statistical Pattern Recognition Toolbox includes: 1) Analysis of linear discriminant functions with implementation examples, 2) Feature extraction using Linear Discriminant Analysis (LDA) algorithms, 3) Probability distribution estimation and clustering techniques, 4) Support Vector Machines and other kernel-based methods with practical code demonstrations.
The Mean Shift-based image segmentation process involves clustering image pixels using the Mean Shift algorithm, where starting points converging to the same mode are grouped into one class and assigned corresponding labels. Classes with insufficient pixels are eliminated, followed by binary segmentation through thresholding.
PCA-KMeans clustering workflow: implementing dimensionality reduction on Wine dataset from UCI repository followed by clustering analysis for pattern discovery
Rapid implementation code for ISODATA clustering algorithm, execute test_isodata to observe test results. This algorithm is ideal for feature classification prior to point pattern matching, featuring adaptive cluster merging/splitting with threshold controls.
A hybrid algorithm integrating Particle Swarm Optimization with K-means clustering, featuring enhanced convergence and cluster optimization capabilities worth implementing.
Implementation of image segmentation through fuzzy c-means (FCM) clustering with detailed algorithm workflow and code integration insights.