Abstract：Aiming at the problems that symbolic attribute data set can not be processed effectively with traditional distance measure method and numerical attribute data set can not be processed effectively by classical rough set method, an improved method of Neighborhood Value Difference Metric (NVDM) was proposed for outlier detection by utilizing the granulation features of neighborhood rough set. Firstly, with attribute values being normalized, the Neighborhood Information System (NIS) was constructed based on optimized Heterogeneous Euclidian-Overlap Metric (HEOM) and neighborhood radius with adaptive characteristic. Secondly, Neighborhood Outlier Factor (NOF) of data object was constructed based on the NVDM. Finally, a Neighborhood Value Difference Metric-based Outlier Detection (NVDMOD) algorithm was designed and implemented, which improves the traditional unordered one by one model via making full use of the idea of ordered binary and nearest neighbor search in computing Single Attribute Neighborhood Cover (SANC). The NVDMOD algorithm was analyzed and compared with existing outlier detection algorithms including NEighborhood outlier Detection (NED) algorithm, DIStance-based outlier detection (DIS) algorithm and K-Nearest Neighbor (KNN) algorithm on UCI standard data sets. The experimental results show that NVDMOD algorithm has much higher adaptability and effectiveness, and it provides a more effective new method for outlier detection of mixed attribute data sets.
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2018, Vol. 38 
