Data clustering is a difficult problem due to the complex and heterogeneous natures of multidimensional data. To improve clustering accuracy, we propose a scheme to capture the lo...
Support vector clustering transforms the data into a high dimensional feature space, where a decision function is computed. In the original space, the function outlines the bounda...
Recent papers and patents in iterative unsupervised learning have emphasized a new trend in clustering. It basically consists of penalizing solutions via weights on the instance po...
We develop a method for clustering all types of belief functions, in particular non-consonant belief functions. Such clustering is done when the belief functions concern multiple ...
act Weighting Framework for Clustering Algorithms Richard Nock Frank Nielsen Recent works in unsupervised learning have emphasized the need to understand a new trend in algorithmi...