Cross-entropy clustering

Cross-entropy clustering

We build a general and highly applicable clustering theory, which we call cross-entropy clustering (shortly CEC) which joins advantages of classical kmeans (easy implementation and speed) with those of EM (affine invariance and ability to adapt to clusters of desired shapes). Moreover, contrary to k-means and EM, CEC finds the optimal number of clusters by automatically removing groups which ca...

Entropy-based Consensus for Distributed Data Clustering

The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...

Split-and-merge Tweak in Cross Entropy Clustering

Detection of Elliptical Shapes via Cross-Entropy Clustering

The problem of finding elliptical shapes in an image will be considered. We discuss the new solution which uses cross-entropy clustering, providing the theoretical background of this approach. The proposed algorithm allows search for ellipses with predefined sizes and position in the space. Moreover, it works well in higher dimensions.

Cross-Entropy Clustering Approach to One-Class Classification

Cross-entropy clustering (CEC) is a density model based clustering algorithm. In this paper we present a possible application of CEC to the one-class classification, which has several advantage over classical approaches based on Expectation Maximization (EM) and Support Vector Machines (SVM). More precisely, we can use various types of gaussian models with lower computational complexity. We tes...