Chameleon Algorithm: Hierarchical Dynamic Clustering

The Chameleon algorithm is a hierarchical dynamic clustering method particularly effective for identifying clusters of arbitrary shapes. Its core methodology involves dynamically merging highly similar subclusters while considering both intra-cluster compactness and inter-cluster connectivity. When implementing the Chameleon algorithm in MATLAB, the following key steps are typically required: Data Preprocessing: Convert raw data into clustering-suitable format, typically by constructing a K-Nearest Neighbor (KNN) graph using functions like `knnsearch` or `fitcknn` to capture local data structure information. This graph representation enables efficient neighborhood analysis. Initial Partitioning: Use efficient partitioning methods like METIS (implemented via third-party tools or `graphpartition` functions) to divide data into smaller subclusters, ensuring high internal coherence within each subcluster. Similarity Calculation: Compute similarity between subclusters by combining Relative Interconnectivity (RI) and Relative Closeness (RC) metrics. This involves graph-based calculations using adjacency matrices and shortest path algorithms (`distances` function) to quantify connectivity strength. Dynamic Merging: Gradually merge subclusters based on similarity scores using iterative merging procedures. Termination conditions can include target cluster numbers (specified via input parameters) or similarity thresholds controlled through while-loop break conditions. Result Validation: Evaluate final clustering quality using validation metrics like silhouette coefficients (`silhouette` function) or other clustering evaluation indices to verify result effectiveness. The MATLAB implementation typically leverages graph theory tools (such as `graph` objects for managing network structures) and optimization techniques like sparse matrix computations (`sparse` function) to enhance efficiency when processing large-scale datasets.