Abstract
Clustering techniques are considered as efficient tools for partitioning data sets in order to get homogeneous clusters of objects. However, the reality is connected to uncertainty by nature, and these standard algorithms of clustering do not deal with this uncertainty pervaded in their parameters. In this paper we develop a clustering method in an uncertain context based on the K-modes method and the belief function theory. This so-called belief K-modes method (BKM) provides a new clustering technique handling uncertainty in the attribute values of objects in both the clusters’ construction task and the classification one.
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Ben Hariz, S., Elouedi, Z., Mellouli, K. (2006). Clustering Approach Using Belief Function Theory. In: Euzenat, J., Domingue, J. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2006. Lecture Notes in Computer Science(), vol 4183. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11861461_18
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DOI: https://doi.org/10.1007/11861461_18
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