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In the text processing field finding the similarity between multiple documents is an important operation. In this paper, we proposed a new similarity measure for document clustering. To figure out the similarity between multiple documents with respect to a feature, our proposed similarity finding measure takes the following cases into account: 1) The selected feature may appear in both documents, 2) the selected feature appears in only one document, and 3) the selected feature appears in none of the documents. In the first case, the documents similarity actually increases as the difference between the selected involved features values are less. Moreover, the involvement of the difference is normally scaled by feature values. However in the second case, a constant value is involved to find the similarity and in the last case, the selected feature are absent between the documents and thus has no contribution to the document similarity. Our proposed measure is extended to estimate the appropriate similarity between two document sets to get effective results with better performance.

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How to Cite
“A NOVEL APPROACH FOR TEXT SIMILARITY MEASURE AND CLASSIFICATION”, IEJRD - International Multidisciplinary Journal, vol. 3, no. 2, p. 7, Mar. 2018.