Concept based clustering for descriptive document classification
We present an approach for improving the relevance of search results by clustering the search results obtained for a query string with the help of a Concept Clustering Algorithm. The Concept Clustering Algorithm combines common phrase discovery and latent semantic indexing techniques to separate sea...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Ubiquity Press
2007-09-01
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| Series: | Data Science Journal |
| Subjects: | |
| Online Access: | http://datascience.codata.org/articles/351 |
| Summary: | We present an approach for improving the relevance of search results by clustering the search results obtained for a query string with the help of a Concept Clustering Algorithm. The Concept Clustering Algorithm combines common phrase discovery and latent semantic indexing techniques to separate search results into meaningful groups. It looks for meaningful phrases to use as cluster labels and then assigns documents to the labels to form groups. The labels assigned to each document cluster provide meaningful information on the various documents available under that cluster. This provides a more interactive and easier way to probe through search results and identifying the relevant documents for the users using the search engine. |
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| ISSN: | 1683-1470 |