Locally incremental visual cluster analysis using Markov random field

Clustering methods are widely deployed in the fields of data mining and pattern recognition. Many of them require the number of clusters as the input, which may not be practical when it is totally unknown. Several existing visual methods for cluster tendency assessment can be used to estimate the number of clusters by displaying the pairwise dissimilarity matrix into an intensity image where objects are reordered to reveal the hidden data structure as dark blocks along the diagonal. A major limitation of the existing methods is that they are not capable to highlight cluster structure with complex clusters. To address this problem, this paper proposes an effective approach by using Markov Random Fields, which updates each object with its local information dynamically and maximizes the global probability measure. The proposed method can be used to determine the cluster tendency and partition data simultaneously. Experimental results on synthetic and real-world datasets demonstrate the effectiveness of the proposed method.