A Proficient Apprehension-Based Mining Replica For Ornamental Text Clustering
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2012, Vol 1, Issue 8
Abstract
Most of the frequent techniques in text mining are based on the arithmetic scrutiny of a idiom, either word or slogan. Arithmetical scrutiny of a term incidence captures the consequence of the term within a manuscript only. However, two provisos can have the similar regularity in their documents, but one term contributes more to the connotation of its sentences than the further term. Thus, the essential text mining mold should designate terms that incarcerate the semantics of text. In this case, the mining replica can incarcerate terms that current the concepts of the condemnation, which leads to innovation of the topic of the document. A novel concept-based mining replica that analyzes terms on the condemnation, document, and corpus levels is introduced. The concept-based mining replica can efficiently distinguish between non imperative terms with esteem to sentence semantics and terms which hold the concepts that symbolize the sentence connotation. The proposed mining replica consists of sentence-based impression scrutiny, document-based perception analysis, corpus-based concept-analysis, and conceptbased resemblance determine. The term which contributes to the condemnation semantics is analyzed on the condemnation, document, and quantity levels relatively than the conventional investigation of the manuscript only. The projected replica can proficiently find considerable toning concepts between documents, according to the semantics of their sentences. The comparison between documents is premeditated based on a new concept-based comparison assess. The proposed correspondence compute takes full improvement of using the perception investigation procedures on the judgment, document, and quantity levels in manipulative the comparison between documents. Large sets of experiments using the projected concept-based mining replica on unusual data sets in text clustering are conducted. The experiments express general contrast between the concept-based investigation and the habitual analysis. Tentative consequences reveal the considerable enrichment of the clustering superiority using the sentence-based, document-based, corpus-based, and united loom concept analysis.
Authors and Affiliations
B. Pavan Kumar, , J. Nagamuneiah,
Keywords
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