K-SVMeans: A hybrid clustering algorithm for multi-type interrelated datasets

Bolelli L., Ertekin Ş., Zhou D., Giles C. L.

IEEE/WIC/ACM International Conference on Web Intelligence, California, Amerika Birleşik Devletleri, 2 - 05 Kasım 2007, ss.198-204 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/wi.2007.61
  • Basıldığı Şehir: California
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.198-204
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

Identification of distinct clusters of documents in text collections has traditionally been addressed by making the assumption that the data instances can only be represented by homogeneous and uniform features. Many real-world data, on the other hand, comprise of multiple types of heterogeneous interrelated components, such as web pages and hyperlinks, online scientific publications and authors and publication venues to name a few. In this paper, we present K-SVMeans, a clustering algorithm for multi-type interrelated datasets that integrates the well known K-Means clustering with the highly popular Support Vector Machines. The experimental results on authorship analysis of two real world web-based datasets show that K-SVMeans can successfully discover topical clusters of documents and achieve better clustering solutions than homogeneous data clustering.