DocumentCode :
2183466
Title :
Improving Web clustering by cluster selection
Author :
Crabtree, Daniel ; Gao, Xiaoying ; Andreae, Peter
Author_Institution :
Sch. of Math., Stat. & Comput. Sci., Victoria Univ. of Wellington, New Zealand
fYear :
2005
fDate :
19-22 Sept. 2005
Firstpage :
172
Lastpage :
178
Abstract :
Web page clustering is a technology that puts semantically related Web pages into groups and is useful for categorizing, organizing, and refining search results. When clustering using only textual information, suffix tree clustering (STC) outperforms other clustering algorithms by making use of phrases and allowing clusters to overlap. One problem of STC and other similar algorithms is how to select a small set of clusters to display to the user from a very large set of generated clusters. The cluster selection method used in STC is flawed in that it does not handle overlapping clusters appropriately. This paper introduces a new cluster scoring function and a new cluster selection algorithm to overcome the problems with overlapping clusters, which are combined with STC to make a new clustering algorithm ESTC. This paper´s experiments show that ESTC significantly outperforms STC and that even with less data ESTC performs similarly to a commercial clustering search engine.
Keywords :
Web sites; classification; pattern clustering; search engines; semantic Web; Web page clustering; cluster scoring function; cluster selection; search engine; suffix tree clustering; textual information; Clustering algorithms; Computer science; Displays; Filters; Internet; Mathematics; Organizing; Search engines; Statistics; Web pages; cluster selection; web clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Web Intelligence, 2005. Proceedings. The 2005 IEEE/WIC/ACM International Conference on
Print_ISBN :
0-7695-2415-X
Type :
conf
DOI :
10.1109/WI.2005.75
Filename :
1517839
Link To Document :
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