关键词: 数据挖掘, 局部可达密度, 数据场, 平均势差, 局部离群因子

Abstract: LOF（Local Outlier Factor） is a classical local outlier detection algorithm based on density. In order to improve the accuracy of the algorithm and dig out the local outlier more accurately, an improvement of LOF outlier detection algorithm based on field data is proposed on the basis of LOF algorithm. Firstly, the potential value is calculated by applying the data field theory to the attribute value of each dimension in the data set. Then a weighted value is added to two random points in each dimension which is larger than the average potential difference by introducing the concept of mean potential difference to improve the accuracy of outlier detection. Experimental results show that the algorithm is feasible and has higher degree of accuracy.

Key words: data mining, local reach ability density, data field, average potential difference, local outlier factor