关键词: 模糊C均值, 自适应蝙蝠算法, 算法优化, 模糊聚类, 异常检测

Abstract: With the rapid development of information technology, data are becoming high-dimensional. How to accurately and efficiently cluster and properly apply these data is particularly important. Although the traditional Fuzzy C-Means clustering algorithm has a good clustering effect, the algorithm still fails to overcome the initialization sensitivity, besides when facing the problem massive high-dimensional network data, that algorithm is easy to fall into local extremum. In order to solve the problem, an adaptive fuzzy algorithm for fuzzy clustering anomaly detection is proposed, and applies it to the anomaly detection. The algorithm adds the concepts of distributed entropy and average bit distance in the classic bat algorithm, which makes the convergence speed of the algorithm greatly improved and can prevent the algorithm falling into local optimal solution. Simulation analysis shows that the algorithm is more stable in clustering and achieves promising detection performance.

Key words: Fuzzy C-Means（FCM）, adaptive bat algorithm, algorithm optimization, fuzzy clustering, anomaly detection