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In many governments and private institutions, one of the major tasks is to select the best project proposals for allocating the fund. These funding organizations select the proposals by submitting them to the reviewers for review. Manual process is too difficult when the number of projects is more. The earlier models introduced ontology based Text mining methods to cluster the proposals of any language...
Text clustering is an important task. Generally, text document clustering methods attempt to segregate the documents into groups where each group represents some topic if the topics are same then they are belonging in the same group if the topics are different then new group can be created with the help of cluster and that different topic store in new group. In this paper has present an clustering...
As semantic information is often missing in text representation, this paper proposes semantic graph structure to represent text and optimize graph structure by semantic similarity matrix. Then calculate the similarity of semantic graph structure by using the maximum common sub-graph of graph theory. Finally, K-means algorithm will be applied to expand Chinese text clustering to improve text clustering...
Document clustering is useful for many research areas such as Text Mining and Information Retrieval. Therefore, it is desirable to be able to cluster documents accurately. The clustering quality depends not only on the clustering algorithm used but also on the way text is represented in the algorithm. Text is typically represented using the All-Words Vector Space Model in text mining applications...
Research project selection is an important task for government and private research funding agencies. When a large number of research proposals are received, it is common to group them according to their similarities in research disciplines. The grouped proposals are then assigned to the appropriate experts for peer review. Current methods for grouping proposals are based on manual matching of similar...
The paper addresses some roles of concept-based representations in document clustering to support knowledge discovery. Computational Intelligence algorithms can benefit from semantic networks in the definition of similarity between pairs of documents. After analyzing the tuning of semantic networks in a systematic fashion, the research defines and evaluates a novel semantic-based metrics, which integrates...
One key step in text mining is the categorization of texts, i.e., to put texts of the same or similar contents into one group so as to distinguish texts of different contents. However, traditional word-frequency-based statistical approaches, such as VSM model, failed to reflect the complicated meaning in texts. This paper ushers in domain ontology and constructs new conceptual vector space model in...
This paper presents a novel technique of document clustering based on frequent concepts. The proposed FCDC (Frequent Concepts based Document Clustering), a clustering algorithm works with frequent concepts rather than frequent itemsets used in traditional text mining techniques. Many well known clustering algorithms deal with documents as bag of words while they ignore the important relationship between...
This work presents the integration of a fuzzy method and text mining to obtain an approach that enables the text documents classification to be closer to the user needs. The aim of this work is to develop a mechanism to reduce the high dimensionality of the attribute-value matrix obtained from the documents and, with this, to manage the imprecision and uncertainty using fuzzy rules to classify text...
This paper presents a novel model for social network analysis in which, rather than analyzing the quantity of relationships (co-authorships, business relations, friendship, etc.), we analyze their communicative content. Text mining and clustering techniques are used to capture the content of communication and to identify the most popular themes. The social analyst is then able to perform a study of...
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