Using Data Mining to Construct an Intelligent Web Search System

In this paper, we present a new ranking algorithm and an intelligent Web search system using data mining techniques to search and analyze Web documents in a more flexible and effective way. Our method takes advantage of the characteristics of Web documents to extract, find, and rank data in a more meaningful manner. We utilize hyperlink structures with Web document content to intelligently rank the retrieved results. It can solve ranking problems of existing algorithms for multiframe Web documents and unrelated linked documents. In addition, we use domain specific ontologies to improve our query process and to rank retrieved Web documents with better semantic notion. Furthermore, we use association rule mining to find the patterns of maximal keyword sets, which represent the main characteristics of the retrieved documents. For subsequent queries, these keywords become recommended sets of query terms for users’ specific needs. Clustering is used to group retrieved documents into distinct sets that can help users make their decisions easier and faster. Experimental results show that our Web search system is indeed effective and efficient.