An Efficient Set-Based Algorithm for Variable Streaming Clustering

Abstract

In this paper, a new algorithm for Data Streaming clustering is proposed, namely the SetClust algorithm. The Data Streaming clustering model focuses on making clustering of the data while it arrives, being useful in many practical applications. The proposed algorithm, unlike other streaming clustering algorithms, is designed to handle cases when there is no available a priori information about the number of clusters to be formed, having as a second objective to discover the best number of clusters needed to represent the points. The SetClust algorithm is based on structures for disjoint-set operations, making the concept of a cluster to be the union of multiple well-formed sets to allow the algorithm to recognize non-spherical patterns even in high dimensional points. This yields to quadratic running time on the number of formed sets. The algorithm itself can be interpreted as an efficient data structure for streaming clustering. Results of the experiments show that the proposed algorithm is highly suitable for clustering quality on well-spread data points.