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Aiming at the problems of the high time complexity and poor detection quality of current outlier detection algorithms, we propose a new outlier detection algorithm based on the improved OPTICS clustering and LOPW. Firstly, the original data set is preprocessed by the improved OPTICS clustering algorithm and the preliminary outlier dataset is obtained by filtering the reachability graph of clustering results. Then, we use the newly defined local outlier factor based on P-weight (LOPW) to calculate the degree of outliers of the objects in the primary outlier dataset. When distances calculated, the leave-one partition information entropy gain is introduced to determine the weight of features, thus improving the precision of outlier detection. Experimental results show that the improved algorithm can improve the computational efficiency and the precision of outlier detection. [ABSTRACT FROM AUTHOR]