Poster Title:

A Clustering Method for Web Data with Multi-Type Interrelated Components

Authors:

• Levent Bolelli (The Pennsylvania State University)
• Seyda Ertekin (The Pennsylvania State University)
• Ding Zhou (The Pennsylvania State University)
• C. Lee Giles (The Pennsylvania State University)

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