Abstract
In the era of information and technology, sensors are widely used to monitor the measured information to support decision making. However, the abnormal data (outlier) is existing in the collected data stream and it would mislead the accuracy of decision making, thus, it is necessary to be detected effectively. Aiming at the problem that the frequent itemset-based outlier detecting method will cost much time in outlier detecting phase, we propose the frequent closed itemset-based outlier detecting method to improve the efficiency of outlier detecting and save much time in outlier detecting stage. Specifically, we mine the frequent closed itemsets with the existing CLOSET algorithm and then design three outlier factors to measure the abnormal degree of each transaction. Then, we propose an outlier detecting method called FCI-Outlier that based on the mined frequent closed itemsets and the designed outlier factors, and the top k transactions that sorting in descending order according to their transaction outlier factor value are judged as the outliers. At last, the public dataset and real data stream are used to verify the efficiency of FCI-Outlier method, and the experimental results show that it is effective in outlier detecting.
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Acknowledgements
This work was supported by Scientific and technological key projects of Xinjiang Production and Construction Corps (Grant No. 2015AC023).
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Hao, S., Cai, S., Sun, R., Li, S. (2019). FCI-Outlier: An Efficient Frequent Closed Itemset-Based Outlier Detecting Approach on Data Stream. 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_13
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DOI: https://doi.org/10.1007/978-981-13-3044-5_13
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