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

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Abstract

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.

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Acknowledgments

Foundation item: This work was funded by the National Natural Science Foundation of China (No.61373134). It was also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Jiangsu Key Laboratory of Meteorological Observation and Information Processing (No.KDXS1105) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET).

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Authors and Affiliations

  1. School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing, 210044, China

    Chunyong Yin & Sun Zhang

  2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, 210044, China

    Chunyong Yin

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  1. Chunyong Yin
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  2. Sun Zhang
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Correspondence to Chunyong Yin.

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Yin, C., Zhang, S. Parallel implementing improved k-means applied for image retrieval and anomaly detection. Multimed Tools Appl 76, 16911–16927 (2017). https://doi.org/10.1007/s11042-016-3638-1

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  • DOI: https://doi.org/10.1007/s11042-016-3638-1

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