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

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. Categories and Subject Descriptors I.5.3 [Pattern Recognition]: Clustering, Algorithms General Terms Algorithms, Experimentation Keywords K-SVMeans, Multi-Type Data Clustering, Online SVM, KMeans

• Type: Conference
• Year: 2007
• Where: WWW
• Authors: Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee Giles
• Keywords: Better Clustering Performance, Homogeneous Data Clustering, Internet Technology, Traditional Clustering Algorithms, WWW 2007