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Relevance Feature Search for Text Mining: A Survey

Rekha R. Kamble, Dattatraya V. Kodavade,

Dept. of Computer Science and Engineering, DKTE’s TEI, Ichalkaranji (An Autonomous Institute), 416115, India.
:10.22362/ijcert/2017/v4/i11/xxxx [UNDER PROCESS]

To determine the quality of user searched documents is a huge challenge in discovering relevance feature. To search the text, document, image, etc. approximately user want relevant features. The techniques earlier used where term based and pattern based. These days clustering methods like partition based, density based and hierarchical is used along with different feature selection method. Extracting terms from the training set for describing relevant features is known as the term-based approach. Low-level support problem is solved by partition based text mining, but it suffers from a large number of noise patterns. Information content in documents is identified by frequent sequential patterns and sequential patterns in the text documents and the useful features for text mining are extracted from this. Extracted terms are classified into three type’s positive terms, general terms and negative terms. To deploy high-level features over low level features positive and negative patterns in text documents are discovered in the present paper.

Rekha R. Kamble and Dattatraya V. Kodavade (2017). Relevance Feature Search for Text Mining: A Survey. International Journal of Computer Engineering In Research Trends, 4(11), 524-528. Retrieved from

Keywords : Text mining, text feature extraction, text classification

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