A highly-usable projected clustering algorithm for gene expression profiles

Abstract

Projected clustering has become a hot research topic due to its ability to cluster high-dimensional data. However, most existing projected clustering algorithms depend on some critical user parameters in determining the relevant attributes of each cluster. In case wrong parameter values are used, the clustering performance will be seriously degraded. Unfortunately, correct parameter values are rarely known in real datasets. In this paper, we propose a projected clustering algorithm that does not depend on user inputs in determining relevant attributes. It responds to the clustering status and adjusts the internal thresholds dynamically. From experimental results, our algorithm shows a much higher usability than the other projected clustering algorithms used in our comparison study. It also works well with a gene expression dataset for studying lymphoma. The high usability of the algorithm and the encouraging results suggest that projected clustering can be a practical tool for analyzing gene expression profiles.