An unsupervised rough clustering method based on gaussian mixture model
Xianghua Fu · Ha'erbin gongye daxue xuebao · 2006
Aiming at resolving randomness and complexity of data statistical distribution,the whole data probability density function is described by Gaussian mixture model in the sight of statistical clustering.A rough clustering analysis method based on Gaussian mixture model is proposed.Firstly,the initial parameters of EM obtained by indiscernibility relation and logic rules generated with rough set theory.Secondly,the maximum likelihood parameters of each component probability density distribution can be estimated by EM iterative computation.Finally,the classification is determined through density distribution probability value.Experimental results show that the new method is effective.Compared with conventional k-means clustering algorithm,it has higher clustering precision and the clustering results described by the rule sets are interpretable and rational.