Abstract
With information proliferation on the Web, how to obtain high-quality information from the Web has been one of hot research topics in many fields like Database, IR as well as AI. Web search engine is the most commonly used tool for information retrieval; however, its current status is far from satisfaction. In this paper, we propose a new approach to cluster search results returned from Web search engine using link analysis. Unlike document clustering algorithms in IR that based on common words/phrases shared between documents, our approach is base on common links shared by pages using co-citation and coupling analysis. We also extend standard clustering algorithm K-means to make it more natural to handle noises and apply it to web search results. By filtering some irrelevant pages, our approach clusters high quality pages into groups to facilitate users’ accessing and browsing. Preliminary experiments and evaluations are conducted to investigate its effectiveness. The experiment results show that clustering on web search results via link analysis is promising.
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Wang, Y., Kitsuregawa, M. (2001). Link Based Clustering of Web Search Results. In: Wang, X.S., Yu, G., Lu, H. (eds) Advances in Web-Age Information Management. WAIM 2001. Lecture Notes in Computer Science, vol 2118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47714-4_21
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DOI: https://doi.org/10.1007/3-540-47714-4_21
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