Abstract
With the development of abnormal behavior analysis technology, measuring the similarity of abnormal behavior has become a core part of abnormal behavior detection. However, there are general problems of central selection distortion and slow iterative convergence with existing clustering-based analysis algorithms. Therefore, this paper proposes an improved clustering-based abnormal behavior analysis algorithm by using K-means. Firstly, an abnormal behavior set is constructed for each user from his or her behavioral data. A weight calculation method for abnormal behaviors and an eigenvalue extraction method for abnormal behavior sets are proposed by using all the behavior sets. Secondly, an improved algorithm is developed, in which we calculate the tightness of all data points and select the initial cluster centers from the data points with high density and low density to improve the clustering effect based on the K-means clustering algorithm. Finally, clustering result of the abnormal behavior is got with the input of the eigenvalues of the abnormal behavior set. The results show that, the proposed algorithm is superior to the traditional clustering algorithm in clustering performance, and can effectively enhance the clustering effect of abnormal behavior.
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Acknowledgements
This work was partly financially supported through grants from the Beijing Science and Technology Plan Project (No. Z171100004717001), Beijing Municipal Natural Science Fund Project (No. L172049), National Natural Science Foundation Project (No. 61671030), Beijing University of Technology Graduate Science and Technology Fund (No. YKJ-2017-00850).
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Zhang, J., Yang, F., Tu, S., Zhang, A. (2018). An Abnormal Behavior Clustering Algorithm Based on K-means. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_52
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DOI: https://doi.org/10.1007/978-3-030-00563-4_52
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