In this paper, we address the problem of entropy-based clustering in the framework of possibility theory. First, we introduce the possibilistic entropy with brief discussion. Then we develop the possibilistic entropy theory for clustering analysis and investigate the general Possibilistic Entropy Clustering (PEC) problems, based on which a Fully Unsupervised Possibilistic Entropy Clustering (FUPEC) algorithm is elaborated in detail with the following advantages: (I) having clearer physical meaning and well-defined mathematical features; (2) automatically determining the number of the clusters; (3) automatically controlling the resolution parameter during the clustering progress; (4) overcoming the sensitivity to initialization and to the noise and outliers. Finally, we illustrate the effectiveness of this novel algorithm with various examples.
['Lei Wang', 'Hongbing Ji', 'Xinbo Gao']
Fully Unsupervised Possibilistic Entropy Clustering