Abstract
Given a database and one single anomalous data point, the Outlying Aspect Mining problem consists in explaining the abnormality of that data point w.r.t. the data population stored in the input database. Thus, the problem requires the discovery of the sets of attributes and associated values that account for the abnormality of a data point within a given data set. In this setting, the abnormality of the data point at hand is stated beforehand, e.g., as the result of some outlier detection techniques (which, for the most part, do not provide information about why the selected data points are actually anomalous). This paper proposes a solution to the OAM problem exploiting a deep learning architecture. Besides explaining the input data point abnormality by singling out the smallest set of pairs attribute-value justifying it, our technique also provides new values for those attributes that would transform the input outlier into an inlier. Several experiments are also presented that assess the effectiveness of our approach.
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References
Abdallah, A., Maarof, M.A., Zainal, A.: Fraud detection system: a survey. J. Netw. Comput. Appl. 68, 90–113 (2016)
Anderson, E.: The species problem in iris. Ann. Mo. Bot. Gard. 23(3), 457–509 (1936). http://www.jstor.org/stable/2394164
Angiulli, F., Fassetti, F., Manco, G., Palopoli, L.: Outlying property detection with numerical attributes. Data Min. Knowl. Disc. 31(1), 134–163 (2016). https://doi.org/10.1007/s10618-016-0458-x
Angiulli, F., Fassetti, F., Nisticò, S.: Finding local explanations through masking models. In: Yin, H., et al. (eds.) IDEAL 2021. LNCS, vol. 13113, pp. 467–475. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91608-4_46
Angiulli, F., Fassetti, F., Palopoli, L.: Detecting outlying properties of exceptional objects. ACM Trans. Database Syst. (TODS) 34(1), 1–62 (2009)
Angiulli, F., Pizzuti, C.: Fast outlier detection in high dimensional spaces. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS, vol. 2431, pp. 15–27. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45681-3_2
Bandaragoda, T.R., Ting, K.M., Albrecht, D., Liu, F.T., Zhu, Y., Wells, J.R.: Isolation-based anomaly detection using nearest-neighbor ensembles. Comput. Intell. 34(4), 968–998 (2018)
Bennett, K.P., Mangasarian, O.L.: Robust linear programming discrimination of two linearly inseparable sets. Optim. Methods Softw. 1(1), 23–34 (1992)
Bhuyan, M.H., Bhattacharyya, D.K., Kalita, J.K.: Network anomaly detection: methods, systems and tools. IEEE Commun. Surv. Tutor. 16(1), 303–336 (2014). https://doi.org/10.1109/SURV.2013.052213.00046
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection for discrete sequences: a survey. IEEE Trans. Knowl. Data Eng. 24(5), 823–839 (2012). https://doi.org/10.1109/TKDE.2010.235
Cunningham, P., Delany, S.J.: K-nearest neighbour classifiers - a tutorial. ACM Comput. Surv. 54(6), 1–25 (2021). https://doi.org/10.1145/3459665
Dang, X.H., Assent, I., Ng, R.T., Zimek, A., Schubert, E.: Discriminative features for identifying and interpreting outliers. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 88–99. IEEE (2014)
Duan, L., Tang, G., Pei, J., Bailey, J., Campbell, A., Tang, C.: Mining outlying aspects on numeric data. Data Min. Knowl. Disc. 29(5), 1116–1151 (2015). https://doi.org/10.1007/s10618-014-0398-2
Duraj, A., Chomatek, L.: Supporting breast cancer diagnosis with multi-objective genetic algorithm for outlier detection. In: Kościelny, J.M., Syfert, M., Sztyber, A. (eds.) DPS 2017. AISC, vol. 635, pp. 304–315. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-64474-5_25
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7(2), 179–188 (1936)
Hauskrecht, M., Batal, I., Valko, M., Visweswaran, S., Cooper, G.F., Clermont, G.: Outlier detection for patient monitoring and alerting. J. Biomed. Inform. 46(1), 47–55 (2013)
Hilal, W., Gadsden, S.A., Yawney, J.: A review of anomaly detection techniques and applications in financial fraud. Expert Syst. Appl. 116429 (2021)
Kriegel, H.-P., Kröger, P., Schubert, E., Zimek, A.: Outlier detection in axis-parallel subspaces of high dimensional data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS (LNAI), vol. 5476, pp. 831–838. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01307-2_86
Kruegel, C., Vigna, G.: Anomaly detection of web-based attacks. In: Proceedings of the 10th ACM Conference on Computer and Communications Security, pp. 251–261 (2003)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422. IEEE (2008)
Micenková, B., Ng, R.T., Dang, X.H., Assent, I.: Explaining outliers by subspace separability. In: 2013 IEEE 13th International Conference on Data Mining, pp. 518–527. IEEE (2013)
Narayanan, V., Bobba, R.B.: Learning based anomaly detection for industrial arm applications. In: Proceedings of the 2018 Workshop on Cyber-Physical Systems Security and PrivaCy, pp. 13–23 (2018)
Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. 54(2), 1–38 (2021). https://doi.org/10.1145/3439950
Samariya, D., Aryal, S., Ting, K.M., Ma, J.: A new effective and efficient measure for outlying aspect mining. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds.) WISE 2020. LNCS, vol. 12343, pp. 463–474. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-62008-0_32
Samariya, D., Ma, J., Aryal, S.: A comprehensive survey on outlying aspect mining methods. arXiv preprint arXiv:2005.02637 (2020)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Routledge, Milton Park (2018)
Steinwart, I., Hush, D., Scovel, C.: A classification framework for anomaly detection. J. Mach. Learn. Res. 6(2) (2005)
Vinh, N.X., Chan, J., Bailey, J., Leckie, C., Ramamohanarao, K., Pei, J.: Scalable outlying-inlying aspects discovery via feature ranking. In: Cao, T., Lim, E.-P., Zhou, Z.-H., Ho, T.-B., Cheung, D., Motoda, H. (eds.) PAKDD 2015. LNCS (LNAI), vol. 9078, pp. 422–434. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18032-8_33
Vinh, N.X., Chan, J., Romano, S., Bailey, J., Leckie, C., Ramamohanarao, K., Pei, J.: Discovering outlying aspects in large datasets. Data Min. Knowl. Disc. 30(6), 1520–1555 (2016). https://doi.org/10.1007/s10618-016-0453-2
Wang, X., Yin, M.: Are explanations helpful? A comparative study of the effects of explanations in AI-assisted decision-making. In: 26th International Conference on Intelligent User Interfaces, pp. 318–328 (2021)
Wells, J.R., Ting, K.M.: A new simple and efficient density estimator that enables fast systematic search. Pattern Recogn. Lett. 122, 92–98 (2019)
Xu, H., et al.: Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications. In: Proceedings of the 2018 World Wide Web Conference, pp. 187–196 (2018)
Zhang, J., Lou, M., Ling, T.W., Wang, H.: Hos-miner: a system for detecting outlying subspaces of high-dimensional data. In: Proceedings of the 30th International Conference on Very Large Data Bases (VLDB 2004), pp. 1265–1268. Morgan Kaufmann Publishers Inc. (2004)
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Angiulli, F., Fassetti, F., Nisticò, S., Palopoli, L. (2022). Outlier Explanation Through Masking Models. In: Chiusano, S., Cerquitelli, T., Wrembel, R. (eds) Advances in Databases and Information Systems. ADBIS 2022. Lecture Notes in Computer Science, vol 13389. Springer, Cham. https://doi.org/10.1007/978-3-031-15740-0_28
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