Clustering feature vectors with mixed numerical and categorical attributes
This paper describes a method for finding a fuzzy membership matrix in case of numerical and categorical features. The set of feature vectors with mixed features is mapped to a set of feature vectors with only real valued components with the condition that the new set of vectors has the same proximi...
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| Format: | Article |
| Language: | English |
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Springer
2008-12-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/1793.pdf |
| _version_ | 1818233651602653184 |
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| author | R.K. Brouwer |
| author_facet | R.K. Brouwer |
| author_sort | R.K. Brouwer |
| collection | DOAJ |
| description | This paper describes a method for finding a fuzzy membership matrix in case of numerical and categorical features. The set of feature vectors with mixed features is mapped to a set of feature vectors with only real valued components with the condition that the new set of vectors has the same proximity matrix as the original feature vectors. This new set of vectors is then clustered using fuzzy c-means. Simulations show the method to be very effective in comparison with other methods. |
| first_indexed | 2024-12-12T11:25:34Z |
| format | Article |
| id | doaj.art-b504ac9ea4b844dd979a5a3f2e6fd101 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-12T11:25:34Z |
| publishDate | 2008-12-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-b504ac9ea4b844dd979a5a3f2e6fd1012022-12-22T00:25:56ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832008-12-011410.2991/ijcis.2008.1.4.1Clustering feature vectors with mixed numerical and categorical attributesR.K. BrouwerThis paper describes a method for finding a fuzzy membership matrix in case of numerical and categorical features. The set of feature vectors with mixed features is mapped to a set of feature vectors with only real valued components with the condition that the new set of vectors has the same proximity matrix as the original feature vectors. This new set of vectors is then clustered using fuzzy c-means. Simulations show the method to be very effective in comparison with other methods.https://www.atlantis-press.com/article/1793.pdfFuzzy clusteringgradient descentcategoricalnominal clusteringfuzzy c-means |
| spellingShingle | R.K. Brouwer Clustering feature vectors with mixed numerical and categorical attributes International Journal of Computational Intelligence Systems Fuzzy clustering gradient descent categorical nominal clustering fuzzy c-means |
| title | Clustering feature vectors with mixed numerical and categorical attributes |
| title_full | Clustering feature vectors with mixed numerical and categorical attributes |
| title_fullStr | Clustering feature vectors with mixed numerical and categorical attributes |
| title_full_unstemmed | Clustering feature vectors with mixed numerical and categorical attributes |
| title_short | Clustering feature vectors with mixed numerical and categorical attributes |
| title_sort | clustering feature vectors with mixed numerical and categorical attributes |
| topic | Fuzzy clustering gradient descent categorical nominal clustering fuzzy c-means |
| url | https://www.atlantis-press.com/article/1793.pdf |
| work_keys_str_mv | AT rkbrouwer clusteringfeaturevectorswithmixednumericalandcategoricalattributes |