Application of Maximum Entropy Fuzzy Clustering Algorithm with Soft computing in Migration Anomaly Detection

Abstract

With the continuous growth of data volume, anomaly detection has become an important link in the data processing process. In view of the maximum entropy Fuzzy clustering algorithm, an anomaly detection method combining Soft computing is proposed. During the process, the K-means algorithm was used to construct the algorithm foundation, followed by the establishment of an objective function for maximum entropy calculation and the introduction of the Hilbert Schmidt independence criterion for variable extraction; Then it conducts Data migration and calculates the exception score. The experiment illustrates that the research method can ultimately decrease to 113 in the Iris dataset when conducting convergence curve testing; When conducting accuracy and purity tests, the accuracy and purity of the research method in the MR dataset were 87.7% and 87.6% respectively; The research methods In the Leaf dataset, the standardized Mutual information index reached 0.6837, and the FM index reached 0.3903; The lowest Davies-Bouldin index of the research method in applied analysis is 0.71; The receiver of the research method operates on the characteristic curve with the largest area enclosed by the abscissa. The results indicate that the research method has high accuracy and computational efficiency in data anomaly detection, and can provide effective technical references for anomaly detection.