Relevance Feature Search for Text Mining: A Survey
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Abstract
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.
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References
Y. Li, A. Algarni, and N. Zhong, “Mining positive and negative patterns for relevance feature discovery,†in Proc. ACM SIGKDD Knowl. Discovery Data Mining, 2010, pp. 753–762.
N. Zhong, Y. Li, and S.-T. Wu, “Effective pattern discovery for text mining,†in IEEE Trans. Knowl. Data Eng., vol. 24, no. 1, pp. 30–44, Jan. 2012.
Z. Zhao, L. Wang, H. Liu, and J. Ye, “On similarity preserving feature selection,†in IEEE Trans. Knowl. Data Eng., vol. 25, no. 3, pp. 619–632, Mar. 2013.
YueLi,, Arif â€Relevance feature discovery for text mining†IEEE transaction on knowledge and data engineering,vol.27,no.6, pp.1656-1669, june2015.
N. Azam and J. Yao, “Comparison of term frequency and document frequency based feature selection metrics in text categorization,â€Expert Syst. Appl., vol. 39, no. 5, pp. 4760–4768,2012.
X. Li and B. Liu, “Learning to classify texts using positive andunlabeled data,†in Proc. 18th Int. Joint Conf. Artif. Intell., 2003,pp. 587–592.
Y. Li, A. Algarni, S.-T. Wu, and Y. Xue, “Mining negative relevancefeedback for information filtering,†in Proc. Web Intell. Intell.Agent Technol., 2009, pp. 606–613.
G. Salton and C. Buckley, “Term-weighting approaches in automatictext retrieval,†in Inf. Process. Manage., vol. 24, no. 5,pp. 513–523, Aug. 1988.
The Porter Stemmer home page (with the original paper and code): http://www.tartarus.org/~martin/PorterStemmer/ 988.
K.Arun .SrinageshandM.Ramesh,â€Twitter Sentiment Analysis on Demonetization tweets in India Using R language.â€International Journal of Computer Engineering in Research Trends., vol.4, no.6, pp. 252- 258, 2017.
TekurVijetha, M.SriLakshmi andDr.S.PremKumar,â€Survey on Collaborative Filtering and content-Based Recommending.â€International Journal of Computer Engineering in Research Trends., vol.2, no.9, pp. 594- 599, 2015.
N.Satish Kumar, SujanBabuVadde,â€Typicality Based Content-BoostedCollaborative Filtering RecommendationFramework.â€International Journal of Computer Engineering in Research Trends., vol.2, no.11, pp. 809-813, 2015
B.Kundan,N.Poorna Chandra Rao and DrS.PremKumar,â€Investigation on Privacy and Secure content of location based Queries.â€International Journal of Computer Engineering in Research Trends., vol.2, no.9, pp. 543-546, 2015.