TY - GEN
T1 - A clustering method for web data with multi-type interrelated components
AU - Bolelli, Levent
AU - Ertekin, Seyda
AU - Zhou, Ding
AU - Giles, C. Lee
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=35348916642&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=35348916642&partnerID=8YFLogxK
U2 - 10.1145/1242572.1242725
DO - 10.1145/1242572.1242725
M3 - Conference contribution
AN - SCOPUS:35348916642
SN - 1595936548
SN - 9781595936547
T3 - 16th International World Wide Web Conference, WWW2007
SP - 1121
EP - 1122
BT - 16th International World Wide Web Conference, WWW2007
T2 - 16th International World Wide Web Conference, WWW2007
Y2 - 8 May 2007 through 12 May 2007
ER -