An optimized hybrid kohonen neural network for ambiguity detection in cluster analysis using simulated annealing
One of the popular tools in the exploratory phase of Data mining and Pattern Recognition is the Kohonen Self Organizing Map (SOM). The SOM maps the input space into a 2-dimensional grid and forms clusters. Recently experiments represented that to catch the ambiguity involved in cluster analysis, it...
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| Format: | Book Section |
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Springer Verlag
2009
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| _version_ | 1796855171156803584 |
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| author | Md. Sap, Mohd. Noor Mohebi, Ehsan |
| author_facet | Md. Sap, Mohd. Noor Mohebi, Ehsan |
| author_sort | Md. Sap, Mohd. Noor |
| collection | ePrints |
| description | One of the popular tools in the exploratory phase of Data mining and Pattern Recognition is the Kohonen Self Organizing Map (SOM). The SOM maps the input space into a 2-dimensional grid and forms clusters. Recently experiments represented that to catch the ambiguity involved in cluster analysis, it is not necessary to have crisp boundaries in some clustering operations. In this paper to overcome the ambiguity involved in cluster analysis, a combination of Rough set Theory and Simulated Annealing is proposed that has been applied on the output grid of SOM. Experiments show that the proposed two-stage algorithm, first using SOM to produce the prototypes then applying rough set and SA in the second stage in order to assign the overlapped data to true clusters they belong to, outperforms the proposed crisp clustering algorithms (i.e. I-SOM) and reduces the errors.
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| first_indexed | 2024-03-05T18:24:43Z |
| format | Book Section |
| id | utm.eprints-12991 |
| institution | Universiti Teknologi Malaysia - ePrints |
| last_indexed | 2024-03-05T18:24:43Z |
| publishDate | 2009 |
| publisher | Springer Verlag |
| record_format | dspace |
| spelling | utm.eprints-129912011-07-12T01:31:01Z http://eprints.utm.my/12991/ An optimized hybrid kohonen neural network for ambiguity detection in cluster analysis using simulated annealing Md. Sap, Mohd. Noor Mohebi, Ehsan QA75 Electronic computers. Computer science One of the popular tools in the exploratory phase of Data mining and Pattern Recognition is the Kohonen Self Organizing Map (SOM). The SOM maps the input space into a 2-dimensional grid and forms clusters. Recently experiments represented that to catch the ambiguity involved in cluster analysis, it is not necessary to have crisp boundaries in some clustering operations. In this paper to overcome the ambiguity involved in cluster analysis, a combination of Rough set Theory and Simulated Annealing is proposed that has been applied on the output grid of SOM. Experiments show that the proposed two-stage algorithm, first using SOM to produce the prototypes then applying rough set and SA in the second stage in order to assign the overlapped data to true clusters they belong to, outperforms the proposed crisp clustering algorithms (i.e. I-SOM) and reduces the errors. Springer Verlag 2009 Book Section PeerReviewed Md. Sap, Mohd. Noor and Mohebi, Ehsan (2009) An optimized hybrid kohonen neural network for ambiguity detection in cluster analysis using simulated annealing. In: Lecture Notes in Business Information Processing. Springer Verlag, Germany, pp. 389-401. ISBN 978-364201346-1 http://dx.doi.org/10.1007/978-3-642-01347-8_33 DOI: 10.1007/978-3-642-01347-8_33 |
| spellingShingle | QA75 Electronic computers. Computer science Md. Sap, Mohd. Noor Mohebi, Ehsan An optimized hybrid kohonen neural network for ambiguity detection in cluster analysis using simulated annealing |
| title | An optimized hybrid kohonen neural network for ambiguity detection in cluster analysis using simulated annealing |
| title_full | An optimized hybrid kohonen neural network for ambiguity detection in cluster analysis using simulated annealing |
| title_fullStr | An optimized hybrid kohonen neural network for ambiguity detection in cluster analysis using simulated annealing |
| title_full_unstemmed | An optimized hybrid kohonen neural network for ambiguity detection in cluster analysis using simulated annealing |
| title_short | An optimized hybrid kohonen neural network for ambiguity detection in cluster analysis using simulated annealing |
| title_sort | optimized hybrid kohonen neural network for ambiguity detection in cluster analysis using simulated annealing |
| topic | QA75 Electronic computers. Computer science |
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