Research of initialization of subspace clustering algorithm in binary data
二元数据子空间聚类算法的初始化研究*

Aiming at the characteristic of high-dimensionality and sparseness in binary data set,proposes the finite mixtures of Bernoulli distributions model for finding projected clusters fast.EM algorithm is the important method of iterative parameters,and the degree of good or bad with EM algorithm lies on initial parameters.As far as the finite mixtures of Bernoulli distributions model,there have been lots of researches about it.However,in EM algorithm,the initial parameters affect the clustering performance directly.Therefore,this paper introduced Binning method and computed parameters through changing the comparability measurement between dates and selection style about core-point,in order to reduce the dependence of the clustering for initial parameters.Experiment demonstrates the algorithm is efficient and accurate.