Extended Mean Field Annealing for Clustering Incomplete Data
Jun Wu, Chihwa Song, Jung Min Kong, Won Don Lee · 2007
Clustering is an very important research topic in knowledge discovery and machine learning. But sometimes the data set for clustering contains vectors missing one or more of the feature values, and is called as incomplete. The incomplete data problem exists in a wide range of field such as computer vision, biological system, and remote sensing. The problem of clustering of incomplete data is considered in this paper. In this paper, we propose a new extended MFA (mean field annealing) algorithm to solve the problem of clustering of incomplete data in the continuous-value state space and show the result of the experiment. The traditional fuzzy clustering methods calculate the centroid vectors of the clusters and then determined the membership probability, and repeat this process until the optimum solution is found. By contrast, the method proposed in this paper perturbs the membership probability, and determines whether to accept the perturbed state or not according to the changes of the energy. The result is compared with the optimal completion strategy fuzzy c-means (FCM) clustering of incomplete data algorithm and shows that the proposed method solves the problem of clustering incomplete data very well and gets a much better result.