Refining Web search engine results using incremental clustering

Ya-Jun Zhang; Zhi-Qiang Liu

doi:10.1002/int.10161

Refining Web search engine results using incremental clustering

Ya-Jun Zhang
, Zhi-Qiang Liu

School of Creative Media

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

10 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	191-199
Journal	International Journal of Intelligent Systems
Volume	19
Issue number	1-2
DOIs	https://doi.org/10.1002/int.10161
Publication status	Published - Jan 2004

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Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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N2 - 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.

AB - 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.

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JO - International Journal of Intelligent Systems

JF - International Journal of Intelligent Systems

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ER -

Refining Web search engine results using incremental clustering

Abstract

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