PCS: An Efficient Clustering Method for High-Dimensional Data

Clustering algorithms play an important role in data analysis and information retrieval. How to obtain a clustering for a large set of highdimensional data suitable for database applications remains a challenge. We devise in this paper a set-theoretic clustering method called PCS (Pairwise Consensus Scheme) for high-dimensional data. Given a large set of d-dimensional data, PCS first constructs ( d p ) clusterings, where p d is a small number (e.g., p = 2 or p = 3) and each clustering is constructed on data projected to a combination of p selected dimensions using an existing p-dimensional clustering algorithm. PCS then constructs, using a greedy pairwise comparison technique based on a recent clustering algorithm [1], a near-optimal consensus clustering from these projected clusterings to be the final clustering of the original data set. We show that PCS incurs only a moderate I/O cost, and the memory requirement is independent of the data size. Finally, we carry out numerical experi...

• Type: Conference
• Year: 2008
• Where: DMIN
• Authors: Wei Li 0011, Cindy Chen, Jie Wang
• Keywords: Clustering Algorithm, Data Mining, DMIN 2008, P-dimensional Clustering Algorithm, Set-theoretic Clustering Method