Online structural clustering based on DBSCAN extension with granular descriptors

Abstract

In online structural clustering, general density-based clustering algorithms have problems of low scalability and high computation cost, especially in big data analysis, this paper proposed a DBSCAN extension algorithm with consideration of granule computing to handle these problems. This algorithm mainly makes use of advantages of DBSCAN and granular descriptors to realize effective and efficient structural online clustering. Frist, to extract structural clusters effectively, DBSCAN is considered as the basic clustering algorithm in this research. Second, since DBSCAN’s results are not numerical for online testing, this paper proposes to apply granule computing (GrC) to construct information granules describing arbitrarily-shaped clusters from DBSCAN. Third, to realize an efficient online structural clustering, especially in big data analysis, a series of granular fuzzy models are built with consideration of structural information, then a rule-based model is formed for guiding online clustering of new testing data. Through the proposed method, the online clustering ability of DBSCAN is developed with reduced computation cost, meanwhile the structural clustering ability is also retained in online testing. Experiments on synthetic data, publicly available data and real-world data are discussed, online testing accuracy and computation time are evaluated to validate the feasibility and effectiveness of the proposed method.