Weighting Method for Feature Selection in K-Means

Joshua Zhexue Huang*, Jun Xu, Michael Ng, Yunming Ye

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingChapterpeer-review

39 Citations (Scopus)

Abstract

The k-means type of clustering algorithms [13, 16] are widely used in real world applications such as marketing research [12] and data mining due to their efficiency in processing large datasets. One unavoidable task of using k-means in real applications is to determine a set of features (or attributes). A common practice is to select features based on business domain knowledge and data exploration. This manual approach is difficult to use, time consuming, and frequently cannot make a right selection. An automated method is needed to solve the feature selection problem in k-means.
Original languageEnglish
Title of host publicationComputational Methods of Feature Selection
EditorsHuan Liu, Hiroshi Motoda
Place of PublicationNew York
PublisherCRC Press
Chapter10
Pages193-209
Number of pages17
Edition1st
ISBN (Electronic)9781584888796, 9780429150418
ISBN (Print)9781584888789
DOIs
Publication statusPublished - 29 Oct 2007

Publication series

NameChapman & Hall/CRC Data Mining and Knowledge Discovery Series

Scopus Subject Areas

  • Mathematics(all)
  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)

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