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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | A FREQUENT TERM BASED TEXT CLUSTERING APPROACH |
| Authors Name: | P.JANANI , S.AMARESAN |
| Unique Id: | IJSDR2006100 |
| Published In: | Volume 5 Issue 6, June-2020 |
| Abstract: | clustering is used to organize the documents into groups. VSM (Vector Space Model) is a technique used to represent the document as a vector. Working with VSM to cluster the documents is easier. The main problem with text documents clustering is very high dimensionality of data. A term in the document represents a dimension. To reduce the dimensions of the document vector space, it is preprocessed. The main techniques involved are stemming and term filtering for dimensions reduction of document vectors. After dimensions reduction, term frequency vectors corresponding to each document are generated, where each cell in the term frequency vector represents frequencies of a term. Using proposed method in the paper, each pair of term frequency vectors are compared to find out the similarity value between every two corresponding documents. In this way, three similarity matrices minimum match, maximum match and average match are generated which are further used in various clustering techniques to produce clusters. Clusters produced using proposed approach are compared with that of clusters produced based on cosine similarity in terms of F-measure. Higher values of F-measure for clusters produced using proposed method shows that proposed algorithm is better.clustering is used to organize the documents into groups. VSM (Vector Space Model) is a technique used to represent the document as a vector. Working with VSM to cluster the documents is easier. The main problem with text documents clustering is very high dimensionality of data. A term in the document represents a dimension. To reduce the dimensions of the document vector space, it is preprocessed. The main techniques involved are stemming and term filtering for dimensions reduction of document vectors. After dimensions reduction, term frequency vectors corresponding to each document are generated, where each cell in the term frequency vector represents frequencies of a term. Using proposed method in the paper, each pair of term frequency vectors are compared to find out the similarity value between every two corresponding documents. In this way, three similarity matrices minimum match, maximum match and average match are generated which are further used in various clustering techniques to produce clusters. Clusters produced using proposed approach are compared with that of clusters produced based on cosine similarity in terms of F-measure. Higher values of F-measure for clusters produced using proposed method shows that proposed algorithm is better. |
| Keywords: | VSM,CLUSTER,APPROACH. |
| Cite Article: | "A FREQUENT TERM BASED TEXT CLUSTERING APPROACH", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.5, Issue 6, page no.605 - 607, June-2020, Available :http://www.ijsdr.org/papers/IJSDR2006100.pdf |
| Downloads: | 000337071 |
| Publication Details: | Published Paper ID: IJSDR2006100 Registration ID:192040 Published In: Volume 5 Issue 6, June-2020 DOI (Digital Object Identifier): Page No: 605 - 607 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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