Parallel implementing improved k-means applied for image retrieval and anomaly detection

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Anomaly detection based on data mining is one of the key technologies to be applied to intelligent detection. K-means is a classic clustering algorithm which is efficient for anomaly detection. Traditional K-means is sensitive to the selection of initial clustering centers. Different initial value can cause different clustering results. We combine improved DD algorithm with information entropy to improve the performance of K-means. Improved K-means can optimize the selection of initial clustering centers; automatically decide the number of clusters and output stable clustering results. After the pretreatment of PCA, the adaptability of improved K-means has a distinct progress. To solve the problem of massive data processing time, we adopt the technology of cloud computing and modify the algorithm for parallel processing. We analyze the performance of improved K-means by using different data sets, KDD Cup99 and public mobile malware data set (i.e. MalGenome). The experimental results illustrate that improved K-means has accurate results and can be applied to anomaly detection in mobile networks. This improved K-means also can be applied for image retrieval by calculating the similarity between each image.