Retriever: Improving Web Search Engine Results Using Clustering
| dc.contributor.author | Jiang, Zhihua | |
| dc.contributor.author | Joshi, Anupam | |
| dc.contributor.author | Krishnapuram, Raghu | |
| dc.contributor.author | Yi, Liyu | |
| dc.date.accessioned | 2019-01-29T15:38:33Z | |
| dc.date.available | 2019-01-29T15:38:33Z | |
| dc.date.issued | 2000-10-24 | |
| dc.description.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. | en_US |
| dc.description.sponsorship | Partial support of this work by grants from National Science Foundation (IIS 9800899 to Krishnapuram, IIS 9801711 to Joshi) is gratefully acknowledged. | en_US |
| dc.description.uri | https://ebiquity.umbc.edu/paper/html/id/97/Retriever-Improving-Web-Search-Engine-Results-Using-Clustering | en_US |
| dc.format.extent | 18 pages | en_US |
| dc.genre | technical reports | en_US |
| dc.identifier | doi:10.13016/m2sm4w-eyu1 | |
| dc.identifier.uri | http://hdl.handle.net/11603/12640 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | 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. | |
| dc.subject | web search engine | en_US |
| dc.subject | clustering | en_US |
| dc.subject | N-gram | en_US |
| dc.subject | vector space | en_US |
| dc.subject | UMBC Ebiquity Research Group | en_US |
| dc.title | Retriever: Improving Web Search Engine Results Using Clustering | en_US |
| dc.type | Text | en_US |