Abstract
This paper proposes a clustering algorithm based on concept of rough computing and Entropy information to cluster objects into manageable smaller groups with similar characteristics or equivalence classes. The concept of rough computing is utilized for handling uncertainty associated with information ambiguity in clustering process. The Entropy information algorithm is employed to transform continuous data into categorical data. The proposed algorithm is capable to cluster different data types; different sources for both numerical and categorical data. The proposed algorithm is implemented and tested for a pharmaceutical company data set as a real case study. The clusters purity is used as a performance measure to evaluate the performance of clusters quality of the proposed algorithm. The comparison study verified that the proposed rough clustering algorithm based on entropy information has the highest clustering quality according to the purity and overall purity evaluation criteria.
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Soliman, O.S., Hassanien, A.E., El-Bendary, N. (2011). A Rough Clustering Algorithm Based on Entropy Information. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślȩzak, D. (eds) Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19644-7_23
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DOI: https://doi.org/10.1007/978-3-642-19644-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19643-0
Online ISBN: 978-3-642-19644-7
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