An efficient ontology based text mining approach using FUZZY SOM and improved genetic algorithm for research project selection

Abstract

Recent technological developments in data mining and knowledge discoveryplay an important role for changing such data into helpful information. Due to the growth and development of this paradigm, discovering the set of meaningful groups of documents become challenging tasks in the active research area. Research and Development phase in research project selection is a decision making job normally started in government and private agencies. Text Mining is an important step for knowledge discovery process. Text mining method is used to mine the unknown information from a huge text document. Ontology is a knowledge storehouse where the terms and concepts are defined with links between those concepts. Ontology s main task is to search the same pattern of text more effectively while searching newlinedocuments. The grouping of proposals for research project selection by ontology based text mining method clusters research project proposals based on their similarities in research area. Though for many research project proposals selection algorithms are available, they are not accurate in selecting the relevant research project proposal. So, various efficient algorithms are proposed in this research work to enhance the ontology based text mining method by selecting the accurate research proposal. newline newline