document: 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 .
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