WCDS: A Two-Phase Weightless Neural System for Data Stream Clustering

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Cardoso, DO (Author) Other; Franca, FMG (Author) Other; João Gama (Author) FEP

Journal

Title: New Generation Computing

Vol. 35 No. 4

Pages: 391-416

ISSN: 0288-3635

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 10 Citations

Other information

Authenticus ID: P-00N-3QT

DOI: 10.1007/s00354-017-0018-y

Abstract (EN): Clustering is a powerful and versatile tool for knowledge discovery, able to provide a valuable information for data analysis in various domains. To perform this task based on streaming data is quite challenging: outdated knowledge needs to be disposed while the current knowledge is obtained from fresh data; since data are continuously flowing, strict efficiency constraints have to be met. This paper presents WCDS, an approach to this problem based on the WiSARD artificial neural network model. This model already had useful characteristics as inherent incremental learning capability and patent functioning speed. These were combined with novel features as an adaptive countermeasure to cluster imbalance, a mechanism to discard expired data, and offline clustering based on a pairwise similarity measure for WiSARD discriminators. In an insightful experimental evaluation, the proposed system had an excellent performance according to multiple quality standards. This supports its applicability for the analysis of data streams.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 26