An Optimized Pruning-Based Outlier Detecting Algorithm

Abstract

An Optimized Pruning-based Outlier Detecting algorithm is proposed based on the density-based outlier detecting algorithm (LOF algorithm). The calculation accuracy and the time complexity of LOF algorithm are not ideal, so two steps are taken to reduce the amount of calculation and improve the calculation accuracy for LOF algorithm. Firstly, using cluster pruning technique to preprocess data set, at the same time filtering the non-outliers based on the differences of cluster models to avoid the error pruning of outliers located at the edge of clusters, different cluster models are output by inputing multiple parameters in the DBSCAN algorithm. Secondly,optimize the query process of the neighborhood (neighbor and k-neighbor). After pruning, local outlier factors are calculated only for the data objects out of clusters. Experimental results show that the algorithm proposed in this paper can improve the outlier detection accuracy, reduce the time complexity and realize the effective local outlier detection.