A novel soft set approach in selecting clustering attribute
Knowledge-Based Systems
The Position of Rough Set in Soft Set: A Topological Approach
International Journal of Applied Metaheuristic Computing
On Quasi Discrete Topological Spaces in Information Systems
International Journal of Artificial Life Research
Computers and Electrical Engineering
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Clustering a set of objects into homogeneous classes is a fundamental operation in data mining. Several cluster analysis techniques have been developed to group objects having similar characteristics. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes. An algorithm termed MMR using classical rough set theory was proposed to deal with problems in clustering categorical data. However, the MMR algorithm fails to handle noisy data as an integral part of databases. In this paper, an alternative technique for clustering noisy categorical data using Variable Precision Rough Set model is proposed. The results show that the technique provides better performance in selecting the clustering attribute.