An Incremental Clustering Approach WITHIN Belief Function Framework

A. Ben Hariz and Z. Elouedi (Tunisia)


Clustering, uncertainty, belief function theory, Belief K modes Method, incremental attribute set.


The Belief K-modes Method (BKM) is a recently devel oped clustering approach handling uncertainty encountered in the attribute values of categorical dataset objects. This uncertainty is represented and managed under the belief function framework. This proposed method, as generally existing clustering ones, starts with a known dataset of ob jects characterized by a given set of attributes. However, there are numerous applications where this attribute set evolves. So, we propose in this paper an Incremental Belief K-modes Method (IBKM), that is able to cluster uncertain data within such dynamic environment. The main objec tive is to efficiently maintain clusters as new attributes are inserted without frequently performing complete recluster ing.

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