title: A new locally weighted K-means for cancer-aided microarray data analysis.
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.
authors.affiliation: School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand. nt.iamon@gmail.com
authors.forename: Natthakan
authors.initials: N
authors.lastname: Iam-On
authors.forename: Tossapon
authors.initials: T
authors.lastname: Boongoen
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D046228:Microarray Analysis / Q000379:methods*; D009369:Neoplasms / Q000453:epidemiology*