Dynamic evidential clustering algorithm

Evidential Clustering: A Review

In evidential clustering, uncertainty about the assignment of objects to clusters is represented by Dempster-Shafer mass functions. The resulting clustering structure, called a credal partition, is shown to be more general than hard, fuzzy, possibilistic and rough partitions, which are recovered as special cases. Three algorithms to generate a credal partition are reviewed. Each of these algori...

Dynamic evidential reasoning algorithm for systems reliability prediction

TS-Models from Evidential Clustering

We study how to derive a fuzzy rule-based classification model using the theoretical framework of belief functions. For this purpose we use the recently proposed Evidential c-means (ECM) to derive Takagi-Sugeno (TS) models solely from data. ECM allocates, for each object, a mass of belief to any subsets of possible clusters, which allows to gain a deeper insight in the data while being robust w...

Dynamic Hierarchical Compact Clustering Algorithm

In this paper we introduce a general framework for hierarchical clustering that deals with both static and dynamic data sets. From this framework, different hierarchical agglomerative algorithms can be obtained, by specifying an inter-cluster similarity measure, a subgraph of the β-similarity graph, and a cover algorithm. A new clustering algorithm called Hierarchical Compact Algorithm and its ...

Evidential clustering of large dissimilarity data

In evidential clustering, the membership of objects to clusters is considered to be uncertain and is represented by Dempster-Shafer mass functions, forming a credal partition. The EVCLUS algorithm constructs a credal partition in such a way that larger dissimilarities between objects correspond to higher degrees of conflict between the associated mass functions. In this paper, we present severa...