Abstract
This paper investigates the problem of meta-clustering real-world retail datasets based on possibility and rough set theories. We propose a crisp then, a soft meta-clustering methods and we compare and analyze the results of both methods using real-world retail datasets. The main aim of this paper is to prove the performance gain of the soft meta-clustering method compared to the crisp one. Our novel methods combine the advantages of the meta-clustering process and the k-modes method under possibilistic and rough frameworks. Our approaches perform a double clustering (or meta-clustering) using two datasets that depend on each other consisting of the retail datasets. It uses for the meta-clustering a modified version of the k-modes method. For the new crisp meta-clustering method, we use the possibilistic k-modes (PKM) and for the soft method, the k-modes under possibilitic and rough frameworks (KM-PR) is applied.
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Ammar, A., Elouedi, Z. (2018). From Crisp to Soft Possibilistic and Rough Meta-clustering of Retail Datasets. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_47
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DOI: https://doi.org/10.1007/978-3-319-76348-4_47
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