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Paper Title:
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 two real world datasets show that K-SVMeans achieves better clustering performance than homogeneous data clustering.

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