Intuitionistic Partition based Conceptual Granulation Topic-Term Modeling

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Dr. D Malathi
Dr. D Malathi
2
D. Malathi
D. Malathi
3
S. Valarmathy
S. Valarmathy
1 Anna University of Technology, Coimbatore

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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.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Dr. D Malathi. 2014. \u201cIntuitionistic Partition based Conceptual Granulation Topic-Term Modeling\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 14 (GJCST Volume 14 Issue C2): .

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Issue Cover
GJCST Volume 14 Issue C2
Pg. 65- 70
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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v1.2

Issue date

May 15, 2014

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English

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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.

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Intuitionistic Partition based Conceptual Granulation Topic-Term Modeling

D. Malathi
D. Malathi
S. Valarmathy
S. Valarmathy

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