Abstract
Data clustering on categorical data pose a difficult challenge since there are no-inherent distance measures between data values. One of the approaches that can be used is by introducing a series of clustering attributes in the categorical data. By this approach, Maximum Total Attribute Relative (MTAR) technique that is based on the attribute relative of soft-set theory has been proposed and proved has better execution time as compared to other equivalent techniques that used the same approach. In this paper, the cluster validity analysis on the technique is explained and discussed. In this analysis, the validity of the clusters produced by MTAR technique is evaluated by the entropy measure using two standards dataset: Soybean (Small) and Zoo from University California at Irvine (UCI) repository. Results show that the clusters produce by MTAR technique have better entropy and improved the clusters validity up to 33%.
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Mamat, R., Noor, A.S.M., Herawan, T., Deris, M.M. (2017). Cluster Validation Analysis on Attribute Relative of Soft-Set Theory. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_1
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DOI: https://doi.org/10.1007/978-3-319-51281-5_1
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