Accelerated univariate sequential clustering

Abstract

Abstract Data clustering is an important task in the field of data mining. In many real applications, clustering algorithms must consider the order of data, resulting in the sequential clustering problem. For instance, analyzing the moving pattern of an object and detecting community structure in a complex network are related to sequential clustering. The constraint of the continuous region prevents previous clustering algorithms from being directly applied to the problem. A dynamic programming algorithm was proposed to address the issue, which returns the optimal sequential clustering. However, it is not scalable. This paper addresses the issue via a greedy stopping condition that prevents the algorithm from continuing to search when it's likely that the best solution has been found. Experimental results on multiple datasets show that the algorithm is much faster than its original solution while the optimality gap is negligible.