Online updating of active function cross-entropy clustering

Gaussian mixture models have many applications in density estimation and data clustering. However, the model does not adapt well to curved and strongly nonlinear data, since many Gaussian components are typically needed to appropriately fit the data that lie around the nonlinear manifold. To solve this problem, the active function cross-entropy clustering (afCEC) method was constructed. In this article, we present an online afCEC algorithm. Thanks to this modification, we obtain a method which is able to remove unnecessary clusters very fast and, consequently, we obtain lower computational complexity. Moreover, we obtain a better minimum (with a lower value of the cost function). The modification allows to process data streams

Split-and-merge Tweak in Cross Entropy Clustering

Part 3: Data Analysis and Information RetrievalInternational audienceIn order to solve the local convergence problem of the Cross Entropy Clustering algorithm, a split-and-merge operation is introduced to escape from local minima and reach a better solution. We describe the theoretical aspects of the method in a limited space, present a few strategies of tweaking the clustering algorithm and compare them with existing solutions. The experiments show that the presented approach increases flexibility and effectiveness of the whole algorithm