Clustering transactional data streams

Authors:
Yanrong Li;Raj P. Gopalan
Affiliations:
Department of Computing, Curtin University of Technology, Bentley, Western Australia;Department of Computing, Curtin University of Technology, Bentley, Western Australia
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

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SCALE: a scalable framework for efficiently clustering transactional data

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Abstract

The challenge of mining data streams is three fold. Firstly, an algorithm for a particular data mining task is subject to the sequential one-pass constraint; secondly, it must work under bounded resources such as memory and disk space; thirdly, it should have capabilities to answer time-sensitive queries. Dealing with transactional data streams is even more challenging due to their high dimensionality and sparseness. In this paper, algorithms for clustering transactional data streams are proposed by incorporating the incremental clustering algorithm INCLUS into the equal-width time window model and the elastic time window model. These algorithms can efficiently cluster a transactional data stream in one pass and answer time sensitive queries at different granularities with limited resources.