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Results diversification for keyword search on XML documents has attracted considerable attentions from research community in recent years. Though search results were diversified from different perspectives in the existing methods, the effects were still far away from satisfactory. This paper proposes a new way to
Topic Detection is a sub-task of Topic Detection and Tracking, its main task is to find and organize topics that system didn't know. By analyzing hundreds of website news reports, we find that usually there exist some keywords in text, and early study didn't pay enough attention to this, we propose a topic detection
called the Associated Keyword Space(ASKS) which is effective for noisy data and projected clustering result from a three-dimensional (3D) sphere to a two dimensional(2D) spherical surface for 2D visualization. One main issue, which affects to the performance of ASKS algorithm is creating the affinity matrix. We use semantic
Co-clustering is a promising technique for summarizing cooccurrence information such as purchase history transactions and document-keyword frequencies. A close connection between fuzzy c-means (FCM) and Gaussian mixture models (GMMs) have been discussed and several extended FCM algorithms, which are induced by the
edges will be built among the blogs which belong to the same result set gotten through the Google blog searching by one keyword. Then the problem of recommender is translated into the clustering of a hyper graph. Our multilevel clustering algorithm is then used to do the segmentation. And we set a new optimization index
keyword, ontology and information-retrieval-based methods. Problems with these approaches include a shortage of high quality ontologies and a loss of semantic information. In addition, there has been little fine-grained improvement in existing approaches to service clustering. In this paper, we present a new approach to
how to eliminate ambiguity more easily and recommend more interested web pages to users. To resolve the above problems, we propose a novel mechanism named SSTAG, and it can recommend a set of Super-tags to users for their choices based on keywords input. As various topics related to the keywords, the Super-tags are
their scalability and efficiency. The problem of extracting knowledge from huge amount of data is recorded as an issue in the medical sector. In this paper, we aim to improve knowledge representation by using MeSH Ontology on medical theses data by analyzing the similarity between the keywords within the theses data and
system (Fexpert) for a research university. Data were undertaken from three sources: (1) researcher's personal profile, (2) graduate school profile and (3) research project profiles. The data were preprocessed and clustered according to each expert's keywords using K-Means algorithm. The proposed system can be used to find
fuzzy Euclidean distance clustering algorithm after using MeSH ontology on medical theses data for better categorization of the keywords within the data.
standardize research keywords. Secondly, frequent item sets with different support degrees are extracted from research proposals based on research ontology. Thirdly, a new measure of similarity degree between two research proposals is developed and then a clustering algorithm is proposed to classify research proposals based on
review. Current methods for grouping proposals are based on manual matching of similar research discipline areas and/or keywords. However, the exact research discipline areas of the proposals cannot often be accurately designated by the applicants due to their subjective views and possible misinterpretations. Therefore
keywords (descriptive terms), then we modify the ontology accordingly by adding the cluster's terms as semantic terms under the “SubSubSubconcept = lecture” to which these documents belong. This research is implemented and evaluated on a real platform HyperManyMedia at Western Kentucky University.
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