Feature Selection for Local Learning Based Clustering

Zeng Hong*, Yiu Ming CHEUNG

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

For most clustering algorithms, their performance will strongly depend on the data representation. In this paper, we attempt to obtain better data representations through feature selection, particularly for the Local Learning based Clustering (LLC) [1]. We assign a weight to each feature, and incorporate it into the built-in regularization of LLC algorithm to take into account of the relevance of each feature for the clustering. Accordingly, the weights are estimated iteratively with the clustering. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparsepromoting penalty, thus the weights for irrelevant features can be driven towards zero. Experiments on several benchmark datasets demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Pages414-425
Number of pages12
DOIs
Publication statusPublished - 2009
Event13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009 - Bangkok, Thailand
Duration: 27 Apr 200930 Apr 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5476 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2009
Country/TerritoryThailand
CityBangkok
Period27/04/0930/04/09

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Feature Selection for Local Learning Based Clustering'. Together they form a unique fingerprint.

Cite this