In this paper, we develop a new clustering method combining the possibility theory with the standard k-modes method (SKM). The proposed method is called KM-PF to express the fact that it is a modification of k-modes algorithm under possibilistic framework. KM-PM incorporates possibilistic theory in two distinct stages in application of the SKM combining the possibilistic k-modes (PKM) and the k-modes using possibilistic membership (KM-PM). First, it deals with uncertain attribute values of instances using possibilistic distributions. Then, it computes the possibilistic membership degrees of each object to all clusters. Experimental results show that the proposed method compares favourably to the SKM, PKM and KM-PM. © 2013 Springer-Verlag.
CITATION STYLE
Ammar, A., Elouedi, Z., & Lingras, P. (2013). The K-modes method under possibilistic framework. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7884 LNAI, pp. 211–217). https://doi.org/10.1007/978-3-642-38457-8_18
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