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Refining web search engine results using incremental clustering
Author(s) -
Zhang YaJun,
Liu ZhiQiang
Publication year - 2004
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.10161
Subject(s) - computer science , cluster analysis , search engine , set (abstract data type) , result set , data mining , information retrieval , group (periodic table) , scheme (mathematics) , machine learning , mathematics , mathematical analysis , chemistry , organic chemistry , programming language
In this article, we present a new solution to improve the Web search performance. Our algorithm is based on a new clustering algorithm that classifies the results of a query from a search engine into subgroups and assigns each group a short series of keywords together with some statistics data. Then, the user may look into the group with the keywords that he/she finds interesting. Compared with the approaches available in the literature, our algorithm does not require the number of groups as the prior knowledge; it starts from a single prototype group and adaptively expands the prototype set based on a self‐spawning splitting scheme until all the groups are finally identified. © 2004 Wiley Periodicals, Inc.