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

Authors :
Cardoso, Douglas
França, Felipe
Gama, João
Source :
New Generation Computing; Oct2017, Vol. 35 Issue 4, p391-416, 26p
Publication Year :
2017

Abstract

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 To perform this task based on streaming data is quite challenging: outdated knowledge needs to be disposed while the current knowledge is obtained from fresh data; since data are continuously flowing, strict efficiency constraints have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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02883635
Volume :
35
Issue :
4
Database :
Complementary Index
Journal :
New Generation Computing
Publication Type :
Academic Journal
Accession number :
125540879
Full Text :
https://doi.org/10.1007/s00354-017-0018-y