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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References
Cao, L., Yang, D., Wang, Q., Yu, Y., Wang, J.: Scalable distance-based outlier detection over high-volume data streams. In: 30th International Conference on Data Engineering, pp. 76–87. IEEE, Chicago (2014)
Angiulli, F., Fassetti, F.: Distance-based outlier queries in data streams: the novel task and algorithms. Data Min. Knowl. Disc. 20(2), 290–324 (2010)
Kontaki, M., Gounaris, A., Papadopoulos, A.N., Tsichlas, K., Manolopoulos, Y.: Efficient and flexible algorithms for monitoring distance-based outliers over data streams. Inf. Syst. 55, 37–53 (2016)
Tang, B., He, H.: A local density-based approach for outlier detection. Neurocomputing 241, 171–180 (2017)
de Vries, T., Chawla, S., Houle, M.E.: Density-preserving projections for large-scale local anomaly detection. Knowl. Inf. Syst. 32(1), 25–52 (2012)
He, Z., Xu, X., Huang, Z.J., Deng, S.: FP-Outlier: frequent pattern based outlier detection. Comput. Sci. Inf. Syst. 2(1), 103–118 (2005)
Zhang, W., Wu, J., Yu, J.: An improved method of outlier detection based on frequent pattern. In: 2th WASE International Conference on Information Engineering, pp. 3–6. IEEE, Beidaihe (2010)
Nori, F., Deypir, M., Sadreddini, M.H.: A sliding window based algorithm for frequent closed itemset mining over data streams. J. Syst. Softw. 86(3), 615–623 (2013)
Pei, J., Han, J., Mao, R.: Closet: an efficient algorithm for mining frequent closed itemsets. In: ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, vol. 4, no. 2, pp. 21–30 (2000)
Cai, S., Sun, R., Cheng, C., Wu, G.: Exception detection of data stream based on improved maximal frequent itemsets mining. In: Xu, M., Qin, Z., Yan, F., Fu, S. (eds.) CTCIS 2017. CCIS, vol. 704, pp. 112–125. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-7080-8_10
Hawkins, D.M.: Identification of Outliers, 11th edn. Chapman and Hall, London (1980)
Lin, F., Le, W., Bo, J.: Research on maximal frequent pattern outlier factor for online high dimensional time-series outlier detection. J. Converg. Inf. Technol. 5(10), 66–71 (2010)
Wisconsin Breast Cancer Data (WBCD) Webpage. https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin. Accessed 12 June 2018
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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