Anomaly detection method based on information entropy clustering

Abstract

The invention discloses an anomaly detection method based on information entropy clustering, which belongs to the field of machine learning and data mining. The anomaly detection algorithm of the invention is based on the idea of the clustering algorithm, and overcomes the shortcomings of the traditional K. Means clustering algorithm randomly selects the initial clustering centers, which easily leads to the clustering results into the local optimal problem. A method based on information entropy to select the initial clustering centers is proposed. The invention provides a method for dividing adata set into data blocks with more than K value, Then the entropy method is used to get the target value function of each data block, and the centroid corresponding to the first k data blocks with the smallest value of the target value function is selected as the initial clustering center. The entropy method is used to ensure the efficiency of selecting the initial clustering center, and the function of anomaly detection is realized in the iterative process of the algorithm. Compared with the traditional K-based clustering algorithm of means, the clustering effect and the anomaly detection ability of the algorithm proposed by the invention are higher than those of the traditional K. Means clustering algorithm. It has certain practical significance.

Description

Technical field [0001] The invention relates to the technical field of machine learning and data mining, in particular to an anomaly detection algorithm based on information entropy clustering. Background technique [0002] With the rapid development of information technology in modern times, some special data that are different from most data in many fields have received widespread attention. These special data are called abnormal data. Anomalies are data that are unique in the data set, which makes people suspect that these data are not biases, but are caused by completely different mechanisms. Commonly used methods of anomaly detection include: anomaly detection method based on statistics, anomaly detection method based on data flow algorithm, and machine learning method based on unsupervised learning. The application of data mining and machine learning in anomaly detection has received widespread attention. Data mining refers to the process of searching for hidden informati...

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Problems solved by technology

[0005] In the present invention, we propose a dynamic clustering method based on information entropy and k-means clustering algorithm for the problem that the clustering effect of the traditional clustering algorithm is easily affected by the initial clustering center. The value method is used to modify the distance function between objects by assigning weights to the clustering objects, using the weighting function value of the initial clustering to select a high-quality initial clustering center, optimizing the initialization process of the algorithm, and based on this Anomaly Detection Algorithm

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