A unified metric for categorical and numerical attributes in data clustering

Yiu Ming CHEUNG, Hong Jia

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

7 Citations (Scopus)

Abstract

Most of the existing clustering approaches are applicable to purely numerical or categorical data only, but not both. In general, it is a nontrivial task to perform clustering on mixed data composed of numerical and categorical attributes because there exists an awkward gap between the similarity metrics for categorical and numerical data. This paper therefore presents a general clustering framework based on the concept of object-cluster similarity and gives a unified similarity metric which can be simply applied to the data with categorical, numerical, or mixed attributes. Accordingly, an iterative clustering algorithm is developed, whose efficacy is experimentally demonstrated on different benchmark data sets.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
Pages135-146
Number of pages12
EditionPART 2
DOIs
Publication statusPublished - 2013
Event17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 - Gold Coast, QLD, Australia
Duration: 14 Apr 201317 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7819 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Country/TerritoryAustralia
CityGold Coast, QLD
Period14/04/1317/04/13

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

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