Application of kohonen neural network and rough approximation for overlapping clusters optimization
In this paper, the Kohonen Self Organizing Map one of the most popular tools in the exploratory phase of pattern recognition is proposed for clustering the input data. Recently researchers found that to have precise and optimized clustering operations and also to capture the ambiguity that comes fro...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2008
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| Online Access: | http://eprints.utm.my/10702/ http://eprints.utm.my/10702/1/MNMSap2008_ApplicationOfKohonenNeuralNetwork.pdf |
| _version_ | 1848892155527954432 |
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| author | Mohebi, E. Md. Sap, Mohd. Noor |
| author_facet | Mohebi, E. Md. Sap, Mohd. Noor |
| author_sort | Mohebi, E. |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | In this paper, the Kohonen Self Organizing Map one of the most popular tools in the exploratory phase of pattern recognition is proposed for clustering the input data. Recently researchers found that to have precise and optimized clustering operations and also to capture the ambiguity that comes from the data sets, it is not necessary to have crisp boundaries in some clustering operation. To overcome the mentioned ambiguity, two variation of cluster approximation (upper and lower) have been applied by Rough set theory. In the first stage the SOM is employed to produce the prototypes then, in the second stage the rough set is applied on the output grid of SOM to detect the ambiguity of SOM clustering. One of the most general optimization techniques (Simulated Annealing) have been adopted to assign the overlapped data to true clusters they belong to by minimizing the uncertainty criteria. Experiments show that the proposed two-level algorithm is more accurate and generates fewer errors as compared with crisp clustering operations. |
| first_indexed | 2025-11-15T21:09:22Z |
| format | Article |
| id | utm-10702 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T21:09:22Z |
| publishDate | 2008 |
| publisher | Penerbit UTM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-107022017-11-01T04:17:23Z http://eprints.utm.my/10702/ Application of kohonen neural network and rough approximation for overlapping clusters optimization Mohebi, E. Md. Sap, Mohd. Noor QA75 Electronic computers. Computer science In this paper, the Kohonen Self Organizing Map one of the most popular tools in the exploratory phase of pattern recognition is proposed for clustering the input data. Recently researchers found that to have precise and optimized clustering operations and also to capture the ambiguity that comes from the data sets, it is not necessary to have crisp boundaries in some clustering operation. To overcome the mentioned ambiguity, two variation of cluster approximation (upper and lower) have been applied by Rough set theory. In the first stage the SOM is employed to produce the prototypes then, in the second stage the rough set is applied on the output grid of SOM to detect the ambiguity of SOM clustering. One of the most general optimization techniques (Simulated Annealing) have been adopted to assign the overlapped data to true clusters they belong to by minimizing the uncertainty criteria. Experiments show that the proposed two-level algorithm is more accurate and generates fewer errors as compared with crisp clustering operations. Penerbit UTM Press 2008-12 Article PeerReviewed application/pdf en http://eprints.utm.my/10702/1/MNMSap2008_ApplicationOfKohonenNeuralNetwork.pdf Mohebi, E. and Md. Sap, Mohd. Noor (2008) Application of kohonen neural network and rough approximation for overlapping clusters optimization. Jurnal Teknologi Maklumat, 20 (4). pp. 17-31. ISSN 0128-3790 |
| spellingShingle | QA75 Electronic computers. Computer science Mohebi, E. Md. Sap, Mohd. Noor Application of kohonen neural network and rough approximation for overlapping clusters optimization |
| title | Application of kohonen neural network and rough approximation for overlapping clusters optimization |
| title_full | Application of kohonen neural network and rough approximation for overlapping clusters optimization |
| title_fullStr | Application of kohonen neural network and rough approximation for overlapping clusters optimization |
| title_full_unstemmed | Application of kohonen neural network and rough approximation for overlapping clusters optimization |
| title_short | Application of kohonen neural network and rough approximation for overlapping clusters optimization |
| title_sort | application of kohonen neural network and rough approximation for overlapping clusters optimization |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/10702/ http://eprints.utm.my/10702/1/MNMSap2008_ApplicationOfKohonenNeuralNetwork.pdf |