As large number of research, educational, institutes are opened day by day so research project selection is an important task for different government and private research funding agencies, Journals, etc. As a large number of research proposals are received, it is common to group them according to their similarities in research disciplines and the grouped proposals are then assigned to the appropriate experts for peer review. Grouping in Current scenario is done by manual matching of similar research discipline areas and/or keyword. As one person not have the whole knowledge of the different research paper, so rich information in the proposals’ full text can be used effectively. By Implementing Text-mining methods to solve the problem by automatically classifying text documents, mainly in English. This paper presents a complete automatic ontology-based text-mining approach where one put paper and year of submission, then it automatically cluster research proposals based on their similarities in research areas. The method is based on use of keywords for creating ontology, then for similarities whole paper is scan based on similarities with the ontology that paper can be classify. It can be efficient and effective for clustering research proposals with English texts as most of research paper are in English language.
The problem of clustering has been studied widely in the database and statistics literature in the context of a wide variety of data mining tasks [50, 54]. The clustering problem is defined to be that of finding groups of similar objects in the data. The similarity between the mining text data objects is measured with the use of a similarity function. The problem of clustering can be very useful in the text domain, where the objects to be clusters can be of different granularities such as documents, paragraphs, sentences or terms.
|IEEE Base paper|
|Doc||Complete Project word file document|
|Read me||Complete read me text file|
|Source Code||Complete Code files|