An Entropy-Based Hierarchical Search Result Clustering Method by Utilizing Augmented Information
| Autor: | Hsin-Wei Hsiao, 蕭新維 |
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| Rok vydání: | 2007 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 95 Because of the improvement of the technology of search engines, and the massively increase of the number of web pages, the results returned by the search engines are always mixed and disordered. Especially for the queries with multiple topics, the mixed and disorderly situation of the search results would be more obvious. The technology of clustering search results with different topics has therefore been extensively developed. For traditional clustering methods, some researchers clustered the document sets using the similarity between two or more documents, or exploited machine learning clustering manner training some documents to get the cluster rules. However, the structure between web pages and general documents are not always the same. It can not confirm that the technologies with good performance on general documents clustering always perform well on the web pages clustering. The search engines can return information of several hundred to thousand of the pages’ titles, snippets and URLs. Almost all of the technologies about search result clustering must attain further information from the contents of the returned lists. Besides, the efficiency issue is also crucial for the clustering of web pages. In web pages clustering it can not use the same technology of analyzing all the contents to calculate its cluster as general document clustering. Supposing that we apply the method of document clustering on web pages clustering, it might waste a lot of time to get the clustered results. Long execution time is not permitted for a real-time clustering system. For this reason, more efficient methods must be developed to conquer these issues. In this paper we propose some methods with better efficiency that will conquer these issues. We improve one of the previous technologies. We utilize and augment information that search engines returned and integrate the augmented information and entropy calculation in the information. We apply several new methods to attain better clustered search results and reduce execution time. From our experiments is also indicate that these methods we proposed would obtain clustered results with high quality. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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