8 research outputs found
Simultaneous Dempster-Shafer clustering and gradual determination of number of clusters using a neural network structure
In this paper we extend an earlier result within Dempster-Shafer theory
["Fast Dempster-Shafer Clustering Using a Neural Network Structure," in Proc.
Seventh Int. Conf. Information Processing and Management of Uncertainty in
Knowledge-Based Systems (IPMU'98)] where several pieces of evidence were
clustered into a fixed number of clusters using a neural structure. This was
done by minimizing a metaconflict function. We now develop a method for
simultaneous clustering and determination of number of clusters during
iteration in the neural structure. We let the output signals of neurons
represent the degree to which a pieces of evidence belong to a corresponding
cluster. From these we derive a probability distribution regarding the number
of clusters, which gradually during the iteration is transformed into a
determination of number of clusters. This gradual determination is fed back
into the neural structure at each iteration to influence the clustering
process.Comment: 6 pages, 10 figure
Creating Prototypes for Fast Classification in Dempster-Shafer Clustering
We develop a classification method for incoming pieces of evidence in Dempster-Shafer theory. This methodology is based on previous work with clustering and specification of originally nonspecific evidence. This methodology is here put in order for fast classification of future incoming pieces of evidence by comparing them with prototypes representing the clusters, instead of making a full clustering of all evidence. This method has a computational complexity of O(M N) for each new piece of evidence, where M is the maximum number of subsets and N is the number of prototypes chosen for each subset. That is, a computational complexity independent of the total number of previously arrived pieces of evidence. The parameters M and N are typically fixed and domain dependent in any application