The improvements and experiments of local spatial outlier detecting algorithm
局部空间离群点算法的改进及其实现

The LOF (local outlier factor) algorithm is a very distinguished local outlier detecting method,which assigns each object an outlier-degree value. In this paper,we present the two improvements of this algorithm. First, the two step improvements was introduced and their time complexity was analysed. Second,when the algorithm identified local outliers, it can consider spatial attributes and non-spatial attribute. The experiments have tested the executing time of the LOF algorithm and its improvements,the performance of computing synthetic and real data set. The experimental results show that is its improvements outperform the LOF algorithm in efficiency and performance.