Bannari Amman Institute of Technology, Anna Universi...To: Author

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CSTP7199
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Analysis represented in vector space model is often used in information retrieval, topic analysis, and automatic classification. However, it hardly deals with fuzzy information and decision-making problems. To account this, Intuitionistic partition based cosine similarity measure between topic/terms and correlation between document/topic are proposed for evaluation. Conceptual granulation is emphasized in the decision matrix expressed conventionally as tf-idf. A local clustering of topic-terms and document-topics results in comparing dependent terms with membership degree using cosine similarity measure and correlation. A preprocessing of documents with intuitionistic fuzzy sets results in efficient classification of large corpus. But it depends on the datasets chosen. The proposed method effectively works well with large sized categorized corpus.
D. Malathi, S. Valarmathy. 2014. "Intuitionistic Partition based Conceptual Granulation Topic-Term Modeling". Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C2).
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
v1.2
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Total Score: 142
Country: India
Subject: Global Journal of Computer Science and Technology
Authors: D. Malathi, S. Valarmathy (PhD/Dr. count: 0)
View Count (all-time): 513
Total Views (Real + Logic): 3059
Total Downloads (simulated): 243
Publish Date: 2014 01, Wed
Monthly Totals (Real + Logic):
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