Efficiently clustering transactional data with weighted coverage density

In this paper, we propose a fast, memory-efficient, and scalable clustering algorithm for analyzing transactional data. Our approach has three unique features. First, we use the concept of Weighted Coverage Density as a categorical similarity measure for efficient clustering of transactional datasets. The concept of weighted coverage density is intuitive and allows the weight of each item in a cluster to be changed dynamically according to the occurrences of items. Second, we develop two transactional data clustering specific evaluation metrics based on the concept of large transactional items and the coverage density respectively. Third, we implement the weighted coverage density clustering algorithm and the two clustering validation metrics using a fully automated transactional clustering framework, called SCALE (Sampling, Clustering structure Assessment, cLustering and domain-specific Evaluation). The SCALE framework is designed to combine the weighted coverage density measure for ...

• Type: Conference
• Year: 2006
• Where: CIKM
• Authors: Hua Yan, Keke Chen, Ling Liu
• Keywords: CIKM 2006, Clustering Algorithms, Information Management, Scalable Clustering Algorithm, Weighted Coverage Density