Clustering and classification of web documents using a graph model

Adam Schenke, Horst Bunke, Mark Last, Abraham Kandel

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In this chapter we provide a summary of our previous work concerning the application of traditional machine learning techniques to data represented by graphs. We show how the fc-means clustering algorithm and the A;-nearest neighbors classification algorithm can easily and intuitively be extended from dealing with vector representations to graph representations. We present some of our experimental results, which confirm that the addition of structural information, not present in vector representations, improves both clustering and classification performance when dealing with web documents.

Original languageEnglish
Title of host publicationHandbook of Pattern Recognition and Computer Vision, 3rd Edition
PublisherWorld Scientific Publishing Co.
Pages287-302
Number of pages16
ISBN (Electronic)9789812775320
ISBN (Print)9812561056, 9789812561053
DOIs
StatePublished - 1 Jan 2005

ASJC Scopus subject areas

  • Computer Science (all)

Fingerprint

Dive into the research topics of 'Clustering and classification of web documents using a graph model'. Together they form a unique fingerprint.

Cite this