Abstract
Outlier detection is a hot issue in data mining, which has plenty of practical applications. Local Outlier Factor algorithm, as a pioneering work of local outlier detection, has been paid much attention. However, it needs to perform the neighbor search with high time complexity and ignores the local distribution of an object within its neighbor. In this work, a novel local outlier detection method based on grid random walk is proposed, which uses random walk to obtain stationary distribution vector of grid information graph. Some grids with small values of stationary distribution vector will be considered as candidate outliers. The outlier detection is performed only on candidate outliers to improve the running efficiency. Then, considering the local distribution of an object within its neighbor, a new local outlier factor is constructed to estimate the abnormal degree of each object. The experimental results indicate that the proposed algorithm has better performance and lower running time than the others.
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Jin F, Chen M, Zhang W, Yuan Y, Wang S (2021) Intrusion detection on internet of vehicles via combining log-ratio oversampling, outlier detection and metric learning. Inf Sci 579:814–831
Li H, Wang B (2020) Xie X (2020) An improved content-based outlier detection method for ics intrusion detection. EURASIP J Wirel Commun Netw 1:1–15
Vasudevan AR, Selvakumar S (2016) Local outlier factor and stronger one class classifier based hierarchical model for detection of attacks in network intrusion detection dataset. Front Comp Sci 10:755–766
Sandosh S, Govindasamy V, Akila G (2020) Enhanced intrusion detection system via agent clustering and classification based on outlier detection. Peer-to-Peer Netw Appl 13(3):1038–1045
Hauskrecht M, Batal I, Valko M, Visweswaran S, Cooper GF, Clermont G (2013) Outlier detection for patient monitoring and alerting. J Biomed Inform 46(1):47–55
Hauskrecht M, Batal I, Hong C, Nguyen Q, Cooper GF, Visweswaran S, Clermont G (2016) Outlier-based detection of unusual patient-management actions: an icu study. J Biomed Inform 64:211–221
Presbitero A, Quax R, Krzhizhanovskaya V, Sloot P (2017) Anomaly detection in clinical data of patients undergoing heart surgery. Proc Comput Sci 108, 99–108 (2017), international Conference on Computational Science, ICCS 2017, 12-14 June 2017, Zurich, Switzerland
Cai L, Thornhill NF, Kuenzel S, Pal BC (2017) Real-time detection of power system disturbances based on \(k\) -nearest neighbor analysis. IEEE Access 5:5631–5639
Anagnostou G, Boem F, Kuenzel S, Pal BC, Parisini T (2018) Observer-based anomaly detection of synchronous generators for power systems monitoring. IEEE Trans Power Syst 33(4):4228–4237
Zhang Q, Wan S, Wang B, Gao DW, Ma H (2019) Anomaly detection based on random matrix theory for industrial power systems. J Syst Architect 95:67–74
Wang B, Mao Z (2019) Outlier detection based on gaussian process with application to industrial processes. Appl Soft Comput 76:505–516
Breunig MM, Kriegel HP, Ng RT, Sander J (2000) Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. pp 93–104. SIGMOD ’00, Association for Computing Machinery, New York, NY, USA
Kim S, Cho NW, Kang B, Kang SH (2011) Fast outlier detection for very large log data. Expert Syst Appl 38(8):9587–9596
Shen J, Liu J, Zhao R, Lin X (2011) A kd-tree-based outlier detection method for airborne lidar point clouds. In: 2011 international symposium on image and data fusion. pp 1–4
Zhang F, Yin F, Huang G (2020) An optimized lof algorithm based on tree structure. In: 2020 3rd international conference on artificial intelligence and big data (ICAIBD). pp 167–171
Li Z, Luo Q, Liu J (2018) Vdod:distributed outlier detection algorithm based on kd-tree. Comput Digit Eng
Pamula R, Deka JK, Nandi S (2011) An outlier detection method based on clustering. In: 2011 second international conference on emerging applications of information technology. pp 253–256
Su S, Xiao L, Ruan L, Gu F, Li S, Wang Z, Xu R (2019) An efficient density-based local outlier detection approach for scattered data. IEEE Access 7:1006–1020
Wang X, Wang X, Wilkes M (2021) Enhancing outlier detection by filtering out core points and border points, pp 173–193. Springer Singapore, Singapore
Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining. p. 226-231. KDD’96, AAAI Press
Schubert E, Zimek A, Kriegel HP (2014) Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data Min Knowl Disc 28(1):190–237
Wang YF, Jiong Y, Su GP, Qian YR (2019) A new outlier detection method based on optics. Sustain Cities Soc 45:197–212
El Meziati M, Ziyati H (2018) Fast outlier detection method based on rough set. In: 2018 9th international symposium on signal, image, video and communications (ISIVC). pp 60–66
Cai S, Sun R, Hao S, Li S, Yuan G (2019) An efficient outlier detection approach on weighted data stream based on minimal rare pattern mining. China Commun 16(10):83–99
Du H, Ye Q, Sun Z, Liu C, Xu W (2021) Fast-odt: a lightweight outlier detection scheme for categorical data sets. IEEE Trans Netw Sci Eng 8(1):13–24
Goldstein M (2012) Fastlof: an expectation-maximization based local outlier detection algorithm. In: Proceedings of the 21st international conference on pattern recognition (ICPR2012). pp 2282–2285
Tang J, Chen Z, Fu AWC, Cheung DW (2002) Enhancing effectiveness of outlier detections for low density patterns. In: Chen MS, Yu PS, Liu B (eds) Advances in knowledge discovery and data mining. Springer, Berlin Heidelberg, pp 535–548
Zhang K, Hutter M, Jin H (2009) A new local distance-based outlier detection approach for scattered real-world data. In: Theeramunkong T, Kijsirikul B, Cercone N, Ho TB (eds) Advances in knowledge discovery and data mining. Springer, Berlin Heidelberg, Berlin, Heidelberg, pp 813–822
Jin W, Tung AKH, Han J, Wang W (2006) Ranking outliers using symmetric neighborhood relationship. In: Ng WK, Kitsuregawa M, Li J, Chang K (eds) Advances in knowledge discovery and data mining. Springer, Berlin Heidelberg, pp 577–593
Yang P, Wang D, Wei Z, Du X, Li T (2019) An outlier detection approach based on improved self-organizing feature map clustering algorithm. IEEE Access 7:115914–115925
Dua D, Graff C (2017) UCI machine learning repository, http://archive.ics.uci.edu/ml
Acknowledgements
This work was supported by Chongqing University Innovation Research Group funding (No. CXQT20015), the Key Science and Technology Research Program of Chongqing Municipal Education Commission (No. KJZD-K201900505), and Research Project of Chongqing Normal University (No. YKC20032).
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She, C., Zeng, S. An enhanced local outlier detection using random walk on grid information graph. J Supercomput 78, 14530–14547 (2022). https://doi.org/10.1007/s11227-022-04459-7
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DOI: https://doi.org/10.1007/s11227-022-04459-7