Kernel neighbor density with parallel computing mechanism for anomaly detection algorithm

Anomaly detection is an important research direction in the field of data mining and industrial dataset preprocess. The paper proposed a kernel neighbor density definition with parallel computing mechanism for anomaly detection algorithm. The kernel neighbor density formula calculates the density of points in high dimensional space. In our definition, we adopt the median operation because the breakdown point of the median is the largest possible. So the definition could be a very robust estimate of the data location, and parallel computing mechanism is introduced to improve the efficiency of algorithms. We use two real datasets and three different kernel functions to evaluate the performance of algorithms. The experiment results confirm that the presented definition of kernel neighbor density improves the performance of algorithms and the Gaussian kernel function has the best effect.