Abstract
With the rapid development of IoT and sensor technologies, data stream is more common in real life and it can provide data assurance for production. However, the abnormal data is usually existed in the collected data stream, and the presence of abnormal data will affect data-based prediction and analysis, therefore, it needs to be detected effectively. The widely distributed sensors make the volume of collected data is very huge, which results the traditional frequent pattern-based abnormal detecting method not suitable for large scale data due to the time cost in abnormal detecting process is expensive. Aimed at this problem, this paper records the data information of the collected data stream into vector structure first, and then proposes a maximal frequent pattern-based abnormal detecting method called MFPM-AD to improve the efficiency of abnormal detecting. Specifically, the maximal frequent patterns are mined instead of frequent patterns to reduce the time cost in the abnormal detecting phase, moreover, three abnormality indexes are designed to measure the abnormal degree of each detected transaction. Then, the abnormal detecting algorithm called MFPM-AD is proposed to effectively detect the implicit abnormal data based on the mined maximal frequent patterns and designed deviation indexes. The experimental results show that our proposed MFPM-AD method can effectively detect the existing implicit abnormal data over data stream.
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Acknowledgements
This work was supported by Scientific and technological key projects of Xinjiang Production & Construction Corps (Grant No. 2015AC023).
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Cai, S., Sun, R., Li, J., Deng, C., Li, S. (2019). Abnormal Detecting over Data Stream Based on Maximal Pattern Mining Technology. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_27
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DOI: https://doi.org/10.1007/978-981-13-3044-5_27
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