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

• Type: Conference
• Year: 2002
• Where: KDD
• Authors: Yiling Yang, Xudong Guan, Jinyuan You
• Keywords: Categorical Data, Data Mining, KDD 2002, Keywords Data Mining, Transactional Data