A clustering method for web data with multi-type interrelated components

Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee Giles

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

1 Scopus citations

Abstract

Traditional clustering algorithms work on "flat" data, making the assumption that the data instances can only be represented by a set of homogeneous and uniform features. Many real world data, however, is heterogeneous in nature, comprising of multiple types of interrelated components. We present a clustering algorithm, K-SVMeans, that integrates the well known K-Means clustering with the highly popular Support Vector Machines(SVM) in order to utilize the richness of data. Our experimental results on authorship analysis of scientific publications show that K-SVMeans achieves better clustering performance than homogeneous data clustering.

Original languageEnglish (US)
Title of host publication16th International World Wide Web Conference, WWW2007
Pages1121-1122
Number of pages2
DOIs
StatePublished - Oct 22 2007
Event16th International World Wide Web Conference, WWW2007 - Banff, AB, Canada
Duration: May 8 2007May 12 2007

Other

Other16th International World Wide Web Conference, WWW2007
CountryCanada
CityBanff, AB
Period5/8/075/12/07

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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