σ-SCLOPE: clustering categorical streams using attribute selection

Authors:
Poh Hean Yap;Kok-Leong Ong
Affiliations:
School of Information Technology, Deakin University, Waurn Ponds, Victoria, Australia;School of Information Technology, Deakin University, Waurn Ponds, Victoria, Australia
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Year:
2005

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Abstract

Clustering is a difficult problem especially when we consider the task in the context of a data stream of categorical attributes. In this paper, we propose σ-SCLOPE, a novel algorithm based on SCLOPE's intuitive observation about cluster histograms. Unlike SCLOPE however, our algorithm consumes less memory per window and has a better clustering runtime for the same data stream in a given window. This positions σ-SCLOPE as a more attractive option over SCLOPE if a minor lost of clustering accuracy is insignificant in the application.