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 language | English (US) |
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Title of host publication | 16th International World Wide Web Conference, WWW2007 |
Pages | 1121-1122 |
Number of pages | 2 |
DOIs | |
State | Published - Oct 22 2007 |
Event | 16th International World Wide Web Conference, WWW2007 - Banff, AB, Canada Duration: May 8 2007 → May 12 2007 |
Other
Other | 16th International World Wide Web Conference, WWW2007 |
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Country/Territory | Canada |
City | Banff, AB |
Period | 5/8/07 → 5/12/07 |
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Software