Penalized and weighted K-means for clustering with scattered objects and prior information in high-throughput biological data

摘要

Motivation: Cluster analysis is one of the most important data mining tools for investigating high-throughput biological data. The existence of many scattered objects that should not be clustered has been found to hinder performance of most traditional clustering algorithms in such a high-dimensional complex situation. Very often, additional prior knowledge from databases or previous experiments is also available in the analysis. Excluding scattered objects and incorporating existing prior information are desirable to enhance the clustering performance.