MATLAB Implementation of DBSCAN Algorithm with Density-Based Clustering
DBSCAN algorithm distinguishes clusters by leveraging density variations in datasets, implemented in MATLAB with epsilon-neighborhood and core point identification.
MATLAB Source Code for Pattern Recognition and Clustering Algorithms
MATLAB-implemented source files for pattern recognition and clustering, featuring comprehensive implementations of major clustering algorithms with configurable parameters and optimization options
MATLAB Implementation of K-Means Clustering Algorithm
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.
DBSCAN Algorithm Source Code for Clustering Applications
MATLAB-implemented DBSCAN algorithm source code for clustering tasks, featuring density-based spatial clustering with noise handling and core point identification
Statistical Analysis of Trained Skin Color Clustering in YCbCr Space
This program performs statistical analysis of trained skin color clustering in the YCbCr color space and constructs a 1D Gaussian model for precise skin segmentation. The implementation involves calculating color distribution patterns and applying probabilistic modeling techniques for accurate skin region identification.
Audio Stream Segmentation and Clustering
This code implements audio segmentation and clustering, built upon existing codebase with proven effectiveness in audio partitioning. It provides essential engineering components for speaker recognition and speech separation tasks, featuring BIC-based segmentation, GMM clustering, and MFCC feature extraction for robust speaker diarization.
Clustering-Based RBF (Radial Basis Function) Neural Network Design Algorithm
A concise clustering-based RBF (Radial Basis Function) neural network design algorithm with implementation insights and parameter optimization strategies
Seven RBF Neural Networks: Source Code Collection with Implementation Approaches
Source code implementations for seven RBF neural networks featuring gradient-based methods, OLS (Ordinary Least Squares), clustering algorithms, k-means clustering, and function approximation techniques for network design and predictive modeling
MATLAB Code Implementation for Radar Signal Sorting
Radar signal sorting using clustering algorithms for unknown signal classification, merging multiple hypothesis classes via similarity coefficients, and performing pattern fusion for multi-mode radars through temporal correlation analysis with MATLAB implementation approaches
FCM Algorithm: A Partition-Based Clustering Algorithm with Fuzzy Logic Implementation
The Fuzzy C-Means (FCM) algorithm is a partition-based clustering method designed to maximize similarity within clusters while minimizing inter-cluster similarity. As an improvement over hard-partitioning C-means algorithms, FCM employs flexible fuzzy partitioning using membership functions. This description covers fuzzy set fundamentals crucial for implementing FCM, including membership degree calculations and iterative optimization procedures in clustering applications.