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Web search users complain of inaccurate results of the current search engines. Most of inaccurate results are from failing to understand user's search goal. This paper proposes a method to mine user's intentions and to build an intention map representing their information needs. It selects intention features from search logs obtained from previous search sessions on a given query and extracts user's...
Text clustering is a hot and essential topic in data mining and information retrieval. This paper proposed a KP-FCM clustering method, which used the key phrases as text features and applied the Fuzzy c-means (FCM) as clustering algorithm. In this method, key phrases were extracted by an algorithm based on suffix array. Experimental results on two standard text clustering benchmark corpuses, OHSUMED...
The amount of online video is increasing tremendously nowadays. For the convenience of information retrieval, video similarity search has become an important research issue in content-based video retrieval. There is still no satisfying scalable fast similarity search method for large database. In order to solve two challenging problems: similarity measure and fast search, a novel efficient video similarity...
As the information available on the internet is growing explosively, this paper proposed a new method of web news summarization via sentence clustering algorithm. It adopted cluster algorithm to cluster all the sentences. Feature fusion will be used to extract summary sentences. Experimental result shows that the proposed summarization method can improve the performance of summary.
The popularity of the Internet has caused a massive increase in the amount of Web pages. The information explosion has led to a growing challenge for information retrieval systems. Document clustering becomes an important process for helping the information retrieval systems organize this vast amount of data. It is believed that grouping similar documents together into clusters will help the users...
Clustering techniques have been used by many intelligent software agents in order to retrieve, filter, and categorize documents available on the World Wide Web. Clustering is also useful in extracting salient features of related Web documents to automatically formulate queries and search for other similar documents on the Web. Traditional clustering algorithms either use a priori knowledge of document...
In this paper, we propose a new semi-supervised clustering methodology to extract topically coherent contents from given Web pages, according to a user's topic interests. It is an effort to resolve low information retrieval performance, caused by one fact that even a single Web page often contains multi-topic related contents. Our evaluation results showed some advantages of our semi-supervised clustering...
The following topics are dealt with: data mining; granular computing; information retrieval; fuzzy set; rough set; feature selection; ontology; pattern clustering and Web page.
Web page clustering is useful for taxonomy design, information extraction, similarity search, and it can assist to the evaluation and visualization of the results of search engines. Therefore, an accurate clustering is a goal in Web mining and Web information extraction. Besides the particular clustering algorithm, the different term weighting functions applied to the selected features to represent...
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