An Efficient EM based Ontology Text-mining to Cluster Proposals for Research Project Selection

Both the internet and the intranets contain more resources and they are called as text documents. Research and Development (R&D) scheme selection is a type of decision-making normally present in government support agencies, universities, research institutes and technology intensive companies. Text Mining has come out as an authoritative technique for extracting the unknown information from large text document. Ontology is defined as a knowledge storehouse in which concepts and conditions are defined in addition to relationships between these concepts. Ontology's build the task of searching alike pattern of text that to be more effectual, efficient and interactive. The present method for combine proposals for selection of research project is proposed by ontology based text mining technique to the data mining approach of cluster research proposals support on their likeness in research area. This proposed method is efficient and effective for clustering research proposals. Though the research proposal regarding particular research area is cannot always be accurate. This study proposed an ontology based text mining to group research proposals, external reviewers based on their research area. The proposed method like Efficient Expectation-Maximization algorithm (EEM) is used to cluster the research proposal and gives better results in more efficient way.

並列關鍵字

Apriori ； document clustering ； ontology

國際替代計量

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