Belief Hierarchical Clustering
This addresses clustering for uncertain databases, which is an incremental improvement over standard methods that ignore uncertainty.
The paper tackles clustering uncertain data by proposing a belief hierarchical clustering method that allows objects to belong to multiple clusters with belief degrees, using pignistic properties for combination, and experiments on real uncertain data show it is a propitious tool.
In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to cluster uncertain data by using a hierarchical clustering defined within the belief function framework. The main objective of the belief hierarchical clustering is to allow an object to belong to one or several clusters. To each belonging, a degree of belief is associated, and clusters are combined based on the pignistic properties. Experiments with real uncertain data show that our proposed method can be considered as a propitious tool.