关键词: 数据挖掘, 异常检测, 核密度估计, 核密度波动, 敏感性分析

Abstract:

Anomaly detection is an important research direction in data mining. Most current density-based algorithms are often based on sample distribution assumptions, are sensitive to the nearest neighbor parameter [k], and cannot detect collective outliers. Aiming at these problems, a kernel density fluctuation algorithm based on kernel density estimation is proposed. The kernel density fluctuation factors that can comprehensively evaluate the fluctuations of nuclear density values within and outside the neighborhood are defined, and detection criteria are developed to identify outliers. This indicator can comprehensively consider the local and global characteristics of the data points, and at the same time help to find collective anomalies. The experimental results on the data set show that the proposed algorithm can achieve better detection results, and at the same time, it is quite robust to the algorithm parameters.

Key words: data mining, anomaly detection, kernel density estimation, kernel-density fluctuation, sensitivity analysis