Robust on-line streaming clustering

Abstract

With the explosion of ubiquitous continuous sensing, on-line streaming clustering continues to attract attention. The requirements are that the streaming clustering algorithm recognize and adapt clusters as the data evolves, that anomalies are detected, and that new clusters are automatically formed as incoming data dictate. In this paper, we extend an earlier approach, called Extended Robust On-Line Streaming Clustering (EROLSC), which utilizes both the Possibilistic C-Means and Gaussian Mixture Decomposition to perform this task. We show the superiority of EROLSC over traditional streaming clustering algorithms on synthetic and real data sets.