Abstract
Identifying outliers in data is an essential assignment in data mining. It aims to find the data that is significantly different from other data in the data streams, and scholars have proposed many outlier detection methods in recent years. Because pattern-based outlier recognition technology clearly illustrates the causes of anomalies, they have received extensive attention. However, the existing pattern-based outlier identification techniques main use frequent patterns or rare patterns with perform the outlier identification operations, which will affect the precision from outlier identification. To improve the exactness of outlier detection on data streams, in this paper, the maximum frequent patterns and minimum rare patterns are combined together and regarded as a whole as the pattern used in the process of outlier detection, and then it redefines the deviation index, so as to perfectly measure the abnormal degrees of outlier in the data streams. Dependent upon those search patterns and characterized deviation index, Novel maximum frequent and minimum rare pattern-based outlier detection method are put forward, that is, specific MFaMRP-OD, which can faultlessly recognize those possibility outliers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kim, K., Choi, Y., Park, J.: Pricing fraud detection in online shopping malls using a finite mixture model. Electron. Commer. Res. Appl. 12(3), 195–207 (2013)
Mamalakis, G., Diou, C., Symeonidis, A., Georgiadis, L.: Of daemons and men: reducing false positive rate in intrusion detection systems with file system footprint analysis. Neural Comput. Appl. 31(11), 7755–7767 (2018). https://doi.org/10.1007/s00521-018-3550-x
Kim, M.S., Kong, H.J., Hong, S.C., Chung, S.H., Hong, J.W.: A flow-based method for abnormal network traffic detection. In: 2004 IEEE/IFIP Network Operations and Management Symposium (IEEE Cat. No.04CH37507), pp. 599–612. IEEE, USA (2004)
Sideris, I.V., Foresti, L., Nerini, D., Germann, U.: NowPrecip: localized precipitation nowcasting in the complex terrain of Switzerland. Q. J. R. Meteorol. Soc. 146(729), 1768–1800 (2020)
Tang, X., Li, G., Chen, G.: Fast detecting outliers over online data streams. In: 2009 International Conference on Information Engineering and Computer Science, pp. 1–4. IEEE, USA (2009)
Said, A.M., Dominic, P.D.D., Faye, I.: Data stream outlier detection approach based on frequent pattern mining technique. Int. J. Bus. Inf. Syst. 20(1), 55–70 (2015)
Toshniwal, D., Yadav, S.: Adaptive outlier detection in streaming time series. In: Proceedings of International Conference on Asia Agriculture and Animal (ICAAA 2011), vol. 13, pp. 186–191. Springer, Heidelberg (2011)
Yuan, J., Wang, Z., Sun, Y., Zhang, W., Jiang, J.: An effective pattern-based Bayesian classifier for evolving data stream. Neurocomputing 295, 17–28 (2018)
Cai, S., Li, L., Li, S., Sun, R., Yuan, G.: An efficient approach for outlier detection from uncertain data streams based on maximal frequent patterns. Expert Syst. Appl. 160, 1–17 (2020)
Cai, S., Li, S., Yuan, G., Hao, S., Sun, R.: MiFI-Outlier: minimal infrequent itemset-based outlier detection approach on uncertain data stream. Knowl.-Based Syst. 191, 1–22 (2020)
Feng, L., Wang, L., Jin, B.: Research on maximal frequent pattern outlier factor for online high dimensional time-series outlier detection. J. Converg. Inf. Technol. 5(10), 66–71 (2010)
Hao, S., Cai, S., Sun, R., Li, S.: FCI-Outlier: an efficient frequent closed itemset-based outlier detecting approach on data stream. In: CCF Conference on Computer Supported Cooperative Work and Social Computing, pp. 371–385. Springer, Heidelberg (2018)
Sweetlin Hemalatha, C., Vaidehi, V., Lakshmi, R.: Minimal infrequent pattern based approach for mining outliers in data streams. Expert Syst. Appl. 42, 1998–2012 (2015)
Aggarwal, C.C., Yu, P.S.: Outlier detection for high dimensional data. Proc. ACM Sigmod May Santa Barbara 30(2), 37–46 (2001)
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(C), 37–53 (2016)
Zhang, L., Lin, J., Karim, R.: Adaptive kernel density-based anomaly detection for nonlinear systems. Knowl.-Based Syst. 139, 50–63 (2018)
Elahi, M., Li, K., Nisar, W., Lv, X., Wang, H.: Efficient clustering-based outlier detection algorithm for dynamic data stream. In: 5th International Conference on Fuzzy Systems and Knowledge Discovery, Shandong, pp. 298–304. IEEE, USA (2008)
He, Z., Xu, X., Huang, Z., Deng, S.: FP-Outlier: Frequent pattern based outlier detection. Computer Science and Information Systems 2(1), 103–118 (2005)
Zhang, W., Wu, J., Yu, J.: An improved method of outlier detection based on frequent pattern. In: WASE International Conference on Information Engineering (ICIE), pp. 3–6. IEEE, USA (2010)
Cai, S., Sun, R., Mu, H., Shi, X., Yuan, G.: 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.) ChineseCSCW 2019. CCIS, vol. 1042, pp. 116–130. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-1377-0_9
Acknowledgment
This research was funded in part by National Key Research and Development Program of China, grant number2016YFB05001805.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shi, X., Cai, S., Sun, R. (2021). Outlier Detection for Sensor Data Streams Based on Maximum Frequent and Minimum Rare Patterns. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_39
Download citation
DOI: https://doi.org/10.1007/978-981-16-2540-4_39
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-2539-8
Online ISBN: 978-981-16-2540-4
eBook Packages: Computer ScienceComputer Science (R0)
