Clustering with Normalized Information Potential Constrained Maximum Entropy Boltzmann Distribution

From a probabilistic perspective, the question of clustering is ´what is the probability that two data samples belong to the same cluster?´´ Accepting the natural preclustering of samples into corresponding modes of the data probability distribution and answering the question posed above for these modes can reduce the problem complexity. Under the maximum entropy principle, a Boltzmann distribution model can be employed to evaluate the required mode-connectivity probabilities. An algorithm is developed using kernel density estimation in this framework. Its performance is demonstrated on benchmark datasets.