AdaUK-means: An ensemble boosting clustering algorithm on uncertain objects

Abstract

This paper considers the problem of clustering uncertain objects whose locations are uncertain and described by probability density functions (pdf). Though K-means has been extended to UK-means for handling uncertain data, most existing works only focus on improving the efficiency of UK-means. However, the clustering quality of UKmeans is rarely considered in existing works. The weights of objects are assumed same in existing works. However, the weights of objects which are far from their cluster representatives should not be the same as the weights of objects which are close to their cluster representatives. Thus, we propose an AdaUK-means to group the uncertain objects by considering the weights of objects in this article. In AdaUK-means, the weights of objects will be adjusted based on the correlation between objects by using Adaboost. If the object pairs are must-link but grouped into different clusters, the weights of the objects will be increased. In our ensemble model, AdaUK-means is run several times, then the objects are assigned by a voting process. Finally, we demonstrate that AdaUK-means performs better than UK-means on both synthetic and real data sets by extensive experiments.