A New Locally Weighted K-Means for Cancer-Aided Microarray Data Analysis

Authors:
Natthakan Iam-On;Tossapon Boongoen
Affiliations:
School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand 57100;Department of Mathematics and Computer Science, Royal Thai Air Force Academy, Bangkok, Thailand 10220
Venue:
Journal of Medical Systems
Year:
2012

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Abstract

Cancer has been identified as the leading cause of death. It is predicted that around 20---26 million people will be diagnosed with cancer by 2020. With this alarming rate, there is an urgent need for a more effective methodology to understand, prevent and cure cancer. Microarray technology provides a useful basis of achieving this goal, with cluster analysis of gene expression data leading to the discrimination of patients, identification of possible tumor subtypes and individualized treatment. Amongst clustering techniques, k-means is normally chosen for its simplicity and efficiency. However, it does not account for the different importance of data attributes. This paper presents a new locally weighted extension of k-means, which has proven more accurate across many published datasets than the original and other extensions found in the literature.