Maximum Minimum Distance Clustering Algorithm

The Maximum Minimum Distance Clustering Algorithm is designed for cluster analysis of one-dimensional or two-dimensional signals, with particular effectiveness in well-separated binary classification scenarios. This algorithm determines cluster formation by calculating maximum and minimum distances between samples through pairwise distance computations. A key implementation aspect involves setting distance thresholds to control cluster density - typically achieved by comparing sample distances against dynamically adjusted critical values. The algorithm's advantages include straightforward implementation using basic distance matrices and threshold comparisons, making it easily understandable and deployable. Furthermore, cluster tightness can be precisely controlled by tuning the maximum and minimum distance thresholds, often implemented through iterative threshold adjustment loops. This flexibility makes the Maximum Minimum Distance Clustering Algorithm an efficient and adaptable clustering method suitable for diverse application domains including signal processing and pattern recognition.