Dynamic clustering with binary social spider algorithm for streaming dataset

Abstract

Technical advancement in various fields like social network, health instruments and astronomical devices poses massive capturing and sensing capacity that enables huge data generations. This demands substantial storage space and voluminous data processing capacity. Streaming data clustering imparts an efficient method for handling this dataset by extracting significant information. In this article, dynamic estimation of clusters in evolving data stream is designed by incorporating swarm optimization technique. One of the recently reported algorithms inspired from the social behavior of spiders residing in huge colonies is reformulated in binary domain. The main contribution is to use the binary social spider optimization (BSSO) for dynamic data clustering of evolving dataset (DSC-BSSO). The proposed work is able to prove efficiency and efficacy as compared to the other recent existing algorithms. BSSO is well tested on various benchmark unimodal, multimodal and binary optimization functions. Results are reported in terms of parametric and nonparametric. The testing of DSC-BSSO is also done on various streaming datasets in terms of time and memory complexity. The proposed work is able to obtain compact and well-separated clusters in less than one-fourth of a minute for about 10,000 samples.