Abstract
This paper investigates the problem of clustering data pervaded by uncertainty. Dealing with uncertainty, in particular, using clustering methods can be of great interest since it helps to make a better decision. In this paper, we combine the k-modes method within the possibility theory in order to obtain a new clustering approach for uncertain categorical data; more precisely we develop the so-called possibilistic k-modes method (PKM) allowing to deal with uncertain attribute values of objects where uncertainty is presented through possibility distributions. Experimental results show good performance on well-known benchmarks.
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Ammar, A., Elouedi, Z. (2011). A New Possibilistic Clustering Method: The Possibilistic K-Modes. In: Pirrone, R., Sorbello, F. (eds) AI*IA 2011: Artificial Intelligence Around Man and Beyond. AI*IA 2011. Lecture Notes in Computer Science(), vol 6934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23954-0_40
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DOI: https://doi.org/10.1007/978-3-642-23954-0_40
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