document: 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 .
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