SNCStream+: Extending a high quality true anytime data stream clustering algorithm. (December 2016)

• Record Type: Journal Article
• Authors: Barddal, Jean Paul; Gomes, Heitor Murilo; Enembreck, Fabrício; Barthès, Jean-Paul
• Is Part Of: Information systems. Volume 62(2016)
• Page Start: 60
• Page End: 73
• Publication Date: 2016-12
• Subjects: Data stream clustering -- Unsupervised learning -- Social networks theory; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2016.06.007
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: Data Stream Clustering is an active area of research which requires efficient algorithms capable of finding and updating clusters incrementally as data arrives. On top of that, due to the inherent evolving nature of data streams, it is expected that algorithms undergo both concept drifts and evolutions, which must be taken into account by the clustering algorithm, allowing incremental clustering updates. In this paper we present the Social Network Clusterer Stream + (SNCStream + ). SNCStream + tackles the data stream clustering problem as a network formation and evolution problem, where instances and micro-clusters form clusters based on homophily. Our proposal has its parameters analyzed and it is evaluated in a broad set of problems against literature baselines. Results show that SNCStream + achieves superior clustering quality (CMM), and feasible processing time and memory space usage when compared to the original SNCStream and other proposals of the literature. Abstract : Highlights: SNCStream + presents high clustering quality accordingly to the Cluster Mapping Measure. SNCStream + possesses diminished computational complexity when compared to its ancestor. SNCStream + is able to diminish the impact of the curse of dimensionality through the usage of specific distance metrics.