LOF算法是一个著名的局部离群点查找方法，该方法赋予了表征每一个空间点偏离程度的数值。但LOF算法存在效率低和性能差的问题，为此对该算法进行了以下两个方面的改进：第一，提出了降低该算法时间复杂度的两步改进方法，并对这两步改进方法的时间复杂度也进行详细分析，第二，使得该算法在查找局部离群点时，不仅考虑了空间属性，也考虑了非空间属性。另外还通过实验测试了LOF算法及其改进方法的时间效率，以及在模拟数据和真实数据情况下的查找离群点的效果。实验结果表明，改进方法具有更好的时间效率和性能。

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.