Mining gene microarray data with adaptive feature scaling

Huimin Geng, Xutao Deng, Hesham Ali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

We propose a new technique, Adaptive Feature Scaling (AFS), to improve the performance of clustering algorithm applied to gene microarray data. In AFS, every feature is assigned multiple weights, each for an individual cluster, and the weights are adaptively updated during the clustering process so that certain features (signals) are strengthened while others (noises) are diminished. Clustering with AFS results in low-noise clusters, each focusing on a small set of signal features. Moreover, the contribution of each feature to each cluster can be revealed by using different feature weights. We apply AFS in conjunction with the Message Passing Clustering (MPC) algorithm to colon cancer data set to show the potential use of AFS in genetics research and medical diagnosis.

Original languageEnglish (US)
Title of host publication2005 IEEE International Conference on Electro Information Technology
StatePublished - 2005
Event2005 IEEE International Conference on Electro Information Technology - Lincoln, NE, United States
Duration: May 22 2005May 25 2005

Publication series

Name2005 IEEE International Conference on Electro Information Technology
Volume2005

Conference

Conference2005 IEEE International Conference on Electro Information Technology
CountryUnited States
CityLincoln, NE
Period5/22/055/25/05

ASJC Scopus subject areas

  • Engineering(all)

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