Abstract. We present a clustering method for continuous data. It defines local clusters into the (primary) data space but derives its similarity measure from the posterior distribu...
In this paper, we initiate a theoretical study of the problem of clustering data under interactive feedback. We introduce a query-based model in which users can provide feedback to...
We present an online adaptive clustering algorithm in a decision tree framework which has an adaptive tree and a code formation layer. The code formation layer stores the represen...
Estimating the optimal number of clusters for a dataset is one of the most essential issues in cluster analysis. An improper pre-selection for the number of clusters might easily ...
Abstract. This paper presents an innovative, adaptive variant of Kohonen’s selforganizing maps called ASOM, which is an unsupervised clustering method that adaptively decides on ...