ISODATA Clustering Experiment for Pattern Recognition Course

In our pattern recognition course laboratory experiments, we conducted ISODATA clustering experiments using custom source programs. ISODATA (Iterative Self-Organizing Data Analysis Technique) clustering represents a sophisticated unsupervised learning method that dynamically groups data points into clusters while automatically determining the optimal number of clusters through iterative merging and splitting operations. Our implementation involved coding key algorithmic components including cluster center initialization, distance metric calculations (typically Euclidean), threshold-based cluster merging when inter-cluster distances fall below specified parameters, and cluster splitting when intra-cluster variances exceed defined limits. The experimental framework incorporated statistical evaluation modules to analyze cluster cohesion and separation metrics. These hands-on experiments significantly enhanced our understanding of clustering methodologies, their practical applications in data structure analysis, and inherent limitations in real-world pattern recognition scenarios, providing valuable insights for both academic learning and subsequent research endeavors.