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On feature selection through clustering

  • University of Massachusetts Boston
  • KDnugets

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

Abstract

We study an algorithm for feature selection that clusters attributes using a special metric and then makes use of the dendrogram of the resulting cluster hierarchy to choose the most relevant attributes. The main interest of our technique resides in the improved understanding of the structure of the analyzed data and of the relative importance of the attributes for the selection process.

Original languageEnglish
Title of host publicationProceedings - Fifth IEEE International Conference on Data Mining, ICDM 2005
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4-7
Number of pages4
ISBN (Print)0769522785, 9780769522784
DOIs
StatePublished - 2005
Event5th IEEE International Conference on Data Mining, ICDM 2005 - Houston, TX, United States
Duration: Nov 27 2005Nov 30 2005

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786

Conference

Conference5th IEEE International Conference on Data Mining, ICDM 2005
Country/TerritoryUnited States
CityHouston, TX
Period11/27/0511/30/05

ASJC Scopus Subject Areas

  • General Engineering

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