Abstract
In recent years, the scale of data stream is becoming much larger in real life. However, the anomaly data often exists in the collected data stream, while the existence of anomaly is a main reason for the decrease of the accuracy of data-based operations. The anomaly data have two main characteristics, that is, appear rarely and deviate much from most data elements, thus, the anomaly detection methods should accurately detect the anomaly data by considering these two attributes. Because the data stream is continuously generated and constantly flowing, thus, the previous static anomaly detection methods are not suitable for processing data streams. In addition, the large amount of data stream makes the time consumption and memory occupation of rare itemset mining phase very high. To effectively solve these problems, this paper first proposes an efficient MRI-Mine method for mining minimum rare itemsets, and then proposes an accurately anomaly detection method called MRI-AD based on anomaly index to identify the implicit anomaly data. The experiments indicate the proposed MRI-Mine method can mine the minimum rare itemsets in less time consumption and memory occupation, and the detection accuracy of MRI-AD method is also competitive.
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Cai, S., Sun, R., Mu, H., Shi, X., Yuan, G. (2019). A Minimum Rare-Itemset-Based Anomaly Detection Method and Its Application on Sensor Data Stream. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_9
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DOI: https://doi.org/10.1007/978-981-15-1377-0_9
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