A Fast Algorithm for Identifying Density-Based Clustering Structures Using a Constraint Graph

Abstract

OPTICS is a state-of-the-art algorithm for visualizing density-based clustering structures of multi-dimensional datasets. However, OPTICS requires iterative distance computations for all objects and is thus computed in O ( n 2 ) time, making it unsuitable for massive datasets. In this paper, we propose constrained OPTICS (C-OPTICS) to quickly create density-based clustering structures that are identical to those by OPTICS. C-OPTICS uses a bi-directional graph structure, which we refer to as the constraint graph, to reduce unnecessary distance computations of OPTICS. Thus, C-OPTICS achieves a good running time to create density-based clustering structures. Through experimental evaluations with synthetic and real datasets, C-OPTICS significantly improves the running time in comparison to existing algorithms, such as OPTICS, DeLi-Clu, and Speedy OPTICS (SOPTICS), and guarantees the quality of the density-based clustering structures.