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2002
ACM

CLOPE: a fast and effective clustering algorithm for transactional data

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CLOPE: a fast and effective clustering algorithm for transactional data
This paper studies the problem of categorical data clustering, especially for transactional data characterized by high dimensionality and large volume. Starting from a heuristic method of increasing the height-to-width ratio of the cluster histogram, we develop a novel algorithm ? CLOPE, which is very fast and scalable, while being quite effective. We demonstrate the performance of our algorithm on two real world datasets, and compare CLOPE with the state-of-art algorithms. Keywords data mining, clustering, categorical data, scalability
Yiling Yang, Xudong Guan, Jinyuan You
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2002
Where KDD
Authors Yiling Yang, Xudong Guan, Jinyuan You
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