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

作者：Iam On Natthakan^*; Boongoen Tossapon

来源：Journal of Medical Systems, 2012, 36(1): S43-S49.

DOI：10.1007/s10916-012-9889-0

• Subspace clustering
• Attribute weighting
• Cancer
• Microarray data analysis

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.

• 出版日期2012-11