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.