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- W2626327908 abstract "This paper proposes two new improvements of the k-modes algorithm under possibilistic and rough frameworks. These new versions of the k-modes deal with uncertainty in the values of attributes and in the belonging of objects to several clusters and handle rough clusters using possibility and rough set theories. In fact, both of the k-modes under possibilistic framework (KM-PF) and the k-modes using possibility and rough set theories (KM-PR) provide successful results when clustering categorical values of attributes under uncertain frameworks. However, they use a random selection of the initial modes which can have a bad impact on the final results. As a good selection of the initial modes can make better the clustering results, our aim through this paper is to propose new methods that improve the KM-PF and KM-PR methods through the selection of initial modes. Besides, we will study and analyze the impact of the good selection of the initial modes on both KM-PF and KM-PR. The test of the new methods that select the initial modes from the most dissimilar objects shows the improvement made in terms of accuracy, iteration number, and execution time." @default.
- W2626327908 created "2017-06-23" @default.
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- W2626327908 date "2017-01-01" @default.
- W2626327908 modified "2023-09-23" @default.
- W2626327908 title "Selection of Initial Modes for Rough Possibilistic K-Modes Methods" @default.
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- W2626327908 doi "https://doi.org/10.1007/978-3-319-60438-1_8" @default.
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