Retriever: Improving Web Search Engine Results Using Clustering

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https://ebiquity.umbc.edu/paper/html/id/97/Retriever-Improving-Web-Search-Engine-Results-Using-Clustering

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http://hdl.handle.net/11603/12640

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

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Date

2000-10-24

Type of Work

technical reports
Text

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This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

web search engine
clustering
N-gram
vector space
UMBC Ebiquity Research Group

Abstract

Web search engine have become increasingly ineffective as the number of documents on the web have proliferated. Typical queries retrieve hundreds of documents, most of which have no relation with what the use was looking for. The objective of this work is to propose new techniques to cluster the results of a query from a search engine into groups. These groups and their associated keywords are presented to the user, who can then look into the URLs for the group(s) that s/he find interesting. N-gram and vector space methods are used to create the dissimilarity matrix for clustering. We compare these distance metrics by clustering the data using a robust fuzzy algorithm and evaluating the results.