EXTENSION OF THE FUZZY C MEANS CLUSTERING ALGORITHM TO FIT WITH THE COMPOSITE GRAPH MODEL FOR WEB DOCUMENT REPRESENTATION
Clustering techniques are mostly unsupervised methods that can be used to organize data into groups based on similarities among the individual data items. Fuzzy c-means (FCM) clustering is one of well known unsupervised clustering techniques, which can also be used for unsupervised web document clus...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Association for the Development of Science, Engineering and Education
2013-12-01
|
| Series: | International Journal of Cognitive Research in Science, Engineering and Education |
| Subjects: | |
| Online Access: | https://ijcrsee.com/index.php/ijcrsee/article/view/20 |
| _version_ | 1829097001293185024 |
|---|---|
| author | Kaushik K. Phukon Hemanta K. Baruah |
| author_facet | Kaushik K. Phukon Hemanta K. Baruah |
| author_sort | Kaushik K. Phukon |
| collection | DOAJ |
| description | Clustering techniques are mostly unsupervised methods that can be used to organize data into groups based on similarities among the individual data items. Fuzzy c-means (FCM) clustering is one of well known unsupervised clustering techniques, which can also be used for unsupervised web document clustering. In this chapter we will introduce a modified method of clustering where the data to be clustered will be represented by graphs instead of vectors or other models. Specifically, we will extend the classical FCM clustering algorithm to work with graphs that represent web documents (Phukon, K. K. (2012), Zadeh, L. A. (1965). Dunn, J. C.(1974)). We wish to use graphs because they can allow us to retain information which is often discarded in simpler models. |
| first_indexed | 2024-04-11T09:05:41Z |
| format | Article |
| id | doaj.art-0af54ce5e70e4b7bace1044801fe6fe0 |
| institution | Directory Open Access Journal |
| issn | 2334-847X 2334-8496 |
| language | English |
| last_indexed | 2024-04-11T09:05:41Z |
| publishDate | 2013-12-01 |
| publisher | Association for the Development of Science, Engineering and Education |
| record_format | Article |
| series | International Journal of Cognitive Research in Science, Engineering and Education |
| spelling | doaj.art-0af54ce5e70e4b7bace1044801fe6fe02022-12-22T04:32:37ZengAssociation for the Development of Science, Engineering and EducationInternational Journal of Cognitive Research in Science, Engineering and Education2334-847X2334-84962013-12-011217317920EXTENSION OF THE FUZZY C MEANS CLUSTERING ALGORITHM TO FIT WITH THE COMPOSITE GRAPH MODEL FOR WEB DOCUMENT REPRESENTATIONKaushik K. Phukon0Hemanta K. Baruah1MCA, Department of Computer Science, Gauhati University, Guwahati- 781014, AssamVice Chancellor, Bodoland University, Kokrajhar-783370, AssamClustering techniques are mostly unsupervised methods that can be used to organize data into groups based on similarities among the individual data items. Fuzzy c-means (FCM) clustering is one of well known unsupervised clustering techniques, which can also be used for unsupervised web document clustering. In this chapter we will introduce a modified method of clustering where the data to be clustered will be represented by graphs instead of vectors or other models. Specifically, we will extend the classical FCM clustering algorithm to work with graphs that represent web documents (Phukon, K. K. (2012), Zadeh, L. A. (1965). Dunn, J. C.(1974)). We wish to use graphs because they can allow us to retain information which is often discarded in simpler models.https://ijcrsee.com/index.php/ijcrsee/article/view/20graphweb documenthard partitionfuzzy partitionfuzzy c- means |
| spellingShingle | Kaushik K. Phukon Hemanta K. Baruah EXTENSION OF THE FUZZY C MEANS CLUSTERING ALGORITHM TO FIT WITH THE COMPOSITE GRAPH MODEL FOR WEB DOCUMENT REPRESENTATION International Journal of Cognitive Research in Science, Engineering and Education graph web document hard partition fuzzy partition fuzzy c- means |
| title | EXTENSION OF THE FUZZY C MEANS CLUSTERING ALGORITHM TO FIT WITH THE COMPOSITE GRAPH MODEL FOR WEB DOCUMENT REPRESENTATION |
| title_full | EXTENSION OF THE FUZZY C MEANS CLUSTERING ALGORITHM TO FIT WITH THE COMPOSITE GRAPH MODEL FOR WEB DOCUMENT REPRESENTATION |
| title_fullStr | EXTENSION OF THE FUZZY C MEANS CLUSTERING ALGORITHM TO FIT WITH THE COMPOSITE GRAPH MODEL FOR WEB DOCUMENT REPRESENTATION |
| title_full_unstemmed | EXTENSION OF THE FUZZY C MEANS CLUSTERING ALGORITHM TO FIT WITH THE COMPOSITE GRAPH MODEL FOR WEB DOCUMENT REPRESENTATION |
| title_short | EXTENSION OF THE FUZZY C MEANS CLUSTERING ALGORITHM TO FIT WITH THE COMPOSITE GRAPH MODEL FOR WEB DOCUMENT REPRESENTATION |
| title_sort | extension of the fuzzy c means clustering algorithm to fit with the composite graph model for web document representation |
| topic | graph web document hard partition fuzzy partition fuzzy c- means |
| url | https://ijcrsee.com/index.php/ijcrsee/article/view/20 |
| work_keys_str_mv | AT kaushikkphukon extensionofthefuzzycmeansclusteringalgorithmtofitwiththecompositegraphmodelforwebdocumentrepresentation AT hemantakbaruah extensionofthefuzzycmeansclusteringalgorithmtofitwiththecompositegraphmodelforwebdocumentrepresentation |