Association for Computing Machinery
Clustering is a data mining technique that groups data into meaningful subclasses, known as clusters, such that it minimizes the intra-differences and maximizes inter-differences of these subclasses. For the purpose of knowledge discovery, it identifies dense and sparse regions and therefore, discovers overall distribution patterns and correlations in the data. Based on the data properties or the task requirements, various clustering algorithms have been developed. OPTICS is a hierarchical density-based data clustering algorithm that discovers arbitrary-shaped clusters and eliminates noise using adjustable reachability distance thresholds.