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Adaptive mining techniques for data streams using algorithm output granularity
Monash University, Melbourne, VIC.
Monash University, Melbourne, VIC.
2003 (English)In: Proceedings of the 2nd Australasian Data Mining Workshop, The University of Technology , 2003Conference paper (Refereed)
Abstract [en]

Mining data streams is an emerging area of research given the potentially large number of business and scientific applications. A significant challenge in analyzing/ mining data streams is the high data rate of the stream. In this paper, we propose a novel approach to cope with the high data rate of incoming data streams. We termed our approach "algorithm output granularity". It is a resource-aware approach that is adaptable to available memory, time constraints, and data stream rate. The approach is generic and applicable to clustering, classification and counting frequent items mining techniques. We have developed a data stream clustering algorithm based on the algorithm output granularity approach. We present this algorithm and discuss its implementation and empirical evaluation. The experiments show  acceptable accuracy accompanied with run-time efficiency. They show that the proposed algorithm outperforms the K-means in terms of running time while preserving the accuracy that our algorithm can achieve.

Place, publisher, year, edition, pages
The University of Technology , 2003.
URN: urn:nbn:se:ltu:diva-37152Local ID: b13d74c0-da3c-11dc-b464-000ea68e967bISBN: 0-975-17241-7OAI: diva2:1010650
Australasian Data Mining Workshop : 08/12/2003 - 12/12/2003
Upprättat; 2003; 20080213 (ysko)Available from: 2016-10-03 Created: 2016-10-03

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