skip to main content
10.1145/1242572.1242725acmconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
Article

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

Published: 08 May 2007 Publication History

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.

References

[1]
A. Bordes, S. Ertekin, J. Weston, and L. Bottou. Fast kernel classifiers with online and active learning. Journal of Machine Learning Research, 6:1579--1619, September 2005.
[2]
I. S. Dhillon and D. S. Modha. Concept decompositions for large sparse text data using clustering. Machine Learning, 42(1):143--175, Jan 2001.

Index Terms

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

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    WWW '07: Proceedings of the 16th international conference on World Wide Web
    May 2007
    1382 pages
    ISBN:9781595936547
    DOI:10.1145/1242572
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 08 May 2007

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. K-Means
    2. K-SVMeans
    3. multi-type data clustering
    4. online SVM

    Qualifiers

    • Article

    Conference

    WWW'07
    Sponsor:
    WWW'07: 16th International World Wide Web Conference
    May 8 - 12, 2007
    Alberta, Banff, Canada

    Acceptance Rates

    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 269
      Total Downloads
    • Downloads (Last 12 months)1
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 19 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media