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

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 scientific publications show that K-SVMeans achieves better clustering performance than homogeneous data clustering.

REFERENCES

• 1: Antoine Bordes , Seyda Ertekin , Jason Weston , Léon Bottou, Fast Kernel Classifiers with Online and Active Learning, The Journal of Machine Learning Research, 6, p.1579-1619, 12/1/2005
• 2: Inderjit S. Dhillon , Dharmendra S. Modha, Concept decompositions for large sparse text data using clustering, Machine Learning, v.42 n.1/2, p.143-175, Jan. 2001 [doi>10.1023/A:1007612920971]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Algorithms

General Terms:
Algorithms, Experimentation

Keywords:
K-Means, K-SVMeans, multi-type data clustering, online SVM