New Local Density Definition Based on Minimum Hyper Sphere for Outlier Mining Algorithm Using in Industrial Databases
Outlier detection is an important procedure in industrial dataset preprocess to guarantee the industrial process operating normally. This paper proposed a new local density definition in the basis of the minimum hyper sphere for outlier mining algorithm. First, the novel local k-density definition of an object is proposed by using the minimum enclosing hyper sphere algorithm. After this, the new k-density definition is adopt in local outlier factor (LOF) algorithm, INFLuenced Outlierness (INFLO) algorithm, and the density-similarity-neighbor-based outlier mining (DSNOF) algorithm constructing ndLOF algorithm, ndINFLO algorithm, and ndDSNOF algorithm. Finally, we evaluate the performance of ndLOF algorithm, ndINFLO algorithm, and ndDSNOF algorithm with LOF algorithm, INFLO algorithm, and DSNOF algorithm on synthetic datasets. The experiments results confirm that the presented definition is meaningful and the outlier mining algorithms improved by the new definition have higher quality of outlier mining.
Outlier mining Minimum Enclosing Hyper Sphere Local-density
Yiwei Yuan Yanbin Zhang Hui Cao Rui Yao
State Key Laboratory of Electrical Insulation and Power Equipment, School of Electrical Engineering, Xi’an jiaotong University,Xi’an, Shaanxi 710049, China
国际会议
长沙
英文
5182-5186
2014-05-31（万方平台首次上网日期，不代表论文的发表时间）