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Building a web‐snippet clustering system based on a mixed clustering method

Building a web‐snippet clustering system based on a mixed clustering method Purpose – Web‐snippet clustering has recently attracted a lot of attention as a means to provide users with a succinct overview of relevant results compared with traditional search results. This paper seeks to research the building of a web‐snippet clustering system, based on a mixed clustering method. Design/methodology/approach – This paper proposes a mixed clustering method to organise all returned snippets into a hierarchical tree. The method accomplishes two main tasks: one is to construct the cluster labels and the other is to build a hierarchical tree. Findings – Five measures were used to measure the quality of clustering results. Based on the results of the experiments, it was concluded that the performance of the system is better than current commercial and academic systems. Originality/value – A high performance system is presented, based on the clustering method. A divisive hierarchical clustering algorithm is also developed to organise all returned snippets into a hierarchical tree. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Online Information Review Emerald Publishing

Building a web‐snippet clustering system based on a mixed clustering method

Online Information Review , Volume 35 (4): 25 – Aug 9, 2011

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Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
1468-4527
DOI
10.1108/14684521111161963
Publisher site
See Article on Publisher Site

Abstract

Purpose – Web‐snippet clustering has recently attracted a lot of attention as a means to provide users with a succinct overview of relevant results compared with traditional search results. This paper seeks to research the building of a web‐snippet clustering system, based on a mixed clustering method. Design/methodology/approach – This paper proposes a mixed clustering method to organise all returned snippets into a hierarchical tree. The method accomplishes two main tasks: one is to construct the cluster labels and the other is to build a hierarchical tree. Findings – Five measures were used to measure the quality of clustering results. Based on the results of the experiments, it was concluded that the performance of the system is better than current commercial and academic systems. Originality/value – A high performance system is presented, based on the clustering method. A divisive hierarchical clustering algorithm is also developed to organise all returned snippets into a hierarchical tree.

Journal

Online Information ReviewEmerald Publishing

Published: Aug 9, 2011

Keywords: Web‐snippet clustering; Precision; Recall; F‐measure; Normalised Google distance; Subtopic reach time; Search results; Search engines; Information searches

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