Abstract：The relative subspace,an attribute set related to outlier,can reduce the impact of "dimensional disaster" effectively.In this paper,the relevant subspace is redefined by Gaussian mixture model and an outlier mining algorithm is presented in the relative subspace.First,each data object′s local dataset is calculated from K-Nearest Neighbors algorithm.Sparse degree matrix,which reflects sparse and dense of data,is generated using the data object′s attribute sparse degree.Second,the relative subspace and unrelated subspace of the data object are identified by Gaussian mixture model and sparse degree matrix,which can avoid the influence of the irrelevant subspace on the measurement of the outlier data.Then,the outlier′s score is calculated in the relevant subspace by the sparseness of the data object′s each dimension and the weights of attribute.And our algorithm can identify outliers as data objects ranked on the first top-N with high outlier′s score.In the end,we conduct extensive experients to validate the correctness and the effectiveness of our algorithm on the synthetic and the UCI data sets.
2018, Vol. 39 
