A rough set approach for selecting clustering attribute
A few of clustering techniques for categorical data exist to group objects having similar characteristics. Some are able to handle uncertainty in the clustering process while others have stability issues. However, the performance of these techniques is an issue due to low accuracy and high computati...
| Main Authors: | , , |
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| Format: | Article |
| Published: |
Elsevier Science Publishers B. V.
2010
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1016/j.knosys.2009.12.003 http://dx.doi.org/10.1016/j.knosys.2009.12.003 |
| Summary: | A few of clustering techniques for categorical data exist to group objects having similar characteristics. Some are able to handle uncertainty in the clustering process while others have stability issues. However, the performance of these techniques is an issue due to low accuracy and high computational complexity. This paper proposes a new technique called maximum dependency attributes (MDA) for selecting clustering attribute. The proposed approach is based on rough set theory by taking into account the dependency of attributes of the database. We analyze and compare the performance of MDA technique with the bi-clustering, total roughness (TR) and min-min roughness (MMR) techniques based on four test cases. The results establish the better performance of the proposed approach. |
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