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

Natthakan Iam-On*, Tossapon Boongoen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (SciVal)

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.

Original languageEnglish
Pages (from-to)S43-549
Number of pages7
JournalJournal of Medical Systems
Volume36 Suppl 1
Early online date28 Oct 2012
DOIs
Publication statusPublished - 01 Nov 2012
Externally publishedYes

Keywords

  • Algorithms
  • Cluster Analysis
  • Data Interpretation, Statistical
  • Gene Expression Profiling/methods
  • Humans
  • Microarray Analysis/methods
  • Neoplasms/epidemiology

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