Explore MATLAB source code curated for "相似性" with clean implementations, documentation, and examples.
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.
Acquire color histograms of targets and compare similarity between two color histograms using the Bhattacharyya distance, including implementation approaches for histogram calculation and distance measurement.
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.