Relevance Feature Discovery (RFD) has been implemented as the next generation architecture of term frequency. In contrast to traditional solutions, where the search in optimizations services are under proper logical and personal controls mechanism. Based on subject-word customizing model, the movement of word meaning creates a personalized searching technology which describes subject-word knowledge. This method Combines multiple meaning of subject words to match and extended. In this proposed work of searching a specific word or sequence of words, or quotations in a large text document is used. It presents a new technique for text document classification using term frequency matrix. Finally the subject word weight is calculated using cosine similarity measure. This work proposes full secure data sharing and access correct person only in implement attribute based access control mechanism to term frequency similarity and Overcome the user revocation problem.
Keywords
Textmining
Text feature classification
Text classification
Authors
M.Mala
How to Cite this Article
M.Mala (2016).
"TERM AND SIMILAR WORD EXTRACTION FOR TEXT DOCUMENTS USING RELEVANCE FEATURE DISCOVERY MODEL".
International Journal of Contemporary Research in Computer Science and Technology,
2(8), pp. 979-982.
