Title: CLUSTERING AND CLASSIFICATION OF WEB DOCUMENTS USING A GRAPH MODEL
Abstract:Handbook of Pattern Recognition and Computer Vision, pp. 287-301 (2005) Free AccessCLUSTERING AND CLASSIFICATION OF WEB DOCUMENTS USING A GRAPH MODELAdam Schenker, Horst Bunke, Mark Last and Abraham K...Handbook of Pattern Recognition and Computer Vision, pp. 287-301 (2005) Free AccessCLUSTERING AND CLASSIFICATION OF WEB DOCUMENTS USING A GRAPH MODELAdam Schenker, Horst Bunke, Mark Last and Abraham KandelAdam SchenkerUniversity of South Florida, 4202 E. Fowler Avenue ENB 118, Tampa, FL 33620, USA, Horst BunkeUniversity of Bern, CH-3012 Bern, Switzerland, Mark LastBen-Gurion University of the Negev, Beer-Sheva 84105, Israel and Abraham KandelUniversity of South Florida, 4202 E. Fowler Avenue ENB 118, Tampa, FL 33620, USAFaculty of Engineering, Tel-Aviv University, Tel-Aviv 69978, Israelhttps://doi.org/10.1142/9789812775320_0016Cited by:0 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail 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 k-means clustering algorithm and the k-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. FiguresReferencesRelatedDetails Handbook of Pattern Recognition and Computer VisionMetrics Downloaded 16 times History PDF downloadRead More
Publication Year: 2005
Publication Date: 2005-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
Access and Citation
Cited By Count: 1
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