Many real-world problems have multiple competing objectives and can often be formulated as multi-objective optimisation problems. Multi-objective evolutionary algorithms have proven very effective in obtaining a set of trade-off solutions for such problems. This research seeks Outliers detection is perceptibly different from or inconsistent with the remaining dataset is a major challenge in real-world multi-objective problem. In this paper, the problem of identifying deviation point in a data set that exhibit non-standard behaviour is referred to as outlier. Outlier detection turns out to be a challenging task due to insufficient data in finding features to describe absolute high data. This paper presents a reference point based outlier detection algorithm using multi-objective evolutionary clustering technique(MOODA). In this algorithm, it assigns a deviation degree to each data point using the sum of distances between referential points to detect distant subspaces where outliers may exist. Finally, experimental studies show that our proposed algorithm is more effective in terms of efficiency and accuracy by using UCI dataset.

Outlier detection,Clustering, Multi-objective optimization, Evolutionary algorithrms

[1] M.H.Marghny and A.I. Taloba, “Outlier Detection using Improved Genetic K-means”. 2011. International Journal of Computer Applications. Vol .28, Issue .11, pp.33-36, 2011.
[2] R.Baklouti, M. Mansouri, M. Nounou, H. Nounou, A.B. Hamida, “Iterated robust kernel fuzzy principal component analysis and application to fault detection”, J. Comput. Sci. Vol. 15, pp. 34–49 2016.
[3] W.D. Fisher, T.K. Camp, V.V. Krzhizhanovskaya, “Anomaly detection in earth dam and levee passive seismic data using support vector machines and automatic feature selection.” J. Comput. Sci, Vol. 20, pp.143–153 ,2017.
[4] M. Sakurada, T. Yairi, Anomaly, “detection using autoencoders with nonlinear dimensionality reduction”, in: Proceedings of the 2014 ACM on Machine Learning for Sensory Data Analysis (MLSDA 2014), pp. 4, 2014
[5] J. T. Andrews, E. J. Morton, L. D. Griffin, “Detecting anomalous data using auto-encoders”, International Journal of Machine Learning and Computing, Vol. 6,Issue.1,pp. 21-26,2016.
[6] S. Wu and S. Wang, “Information-theoretic outlier detection for large-scale categorical data,” IEEE Trans on Knowledge and Data Engineering (TKDE), Vol. 25, Issue. 3, pp. 589–602, 2013.
[7] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection for discrete sequences: A survey,” IEEE Trans on Knowledge and Data Engineering (TKDE), Vol. 24, Issue. 5, pp. 823–839, 2012.
[8] M. Hubert,P.J.Rousseeuw, P. Segaert, “Multivariate functional outlier detection.”, Stat. Methods Appl. Vol. 24,Issue. 2, pp. 177–202,2015.
[9] S. Hido, Y. Tsuboi, H. Kashima, M. Sugiyama, and T. Kanamori, “Statistical outlier detection using direct density ratio estimation,” Knowledge and Information Systems”, Vol. 26, Issue. 2, pp. 309–336, 2011.
[10] B. Perozzi, L. Akoglu,P. Iglesias Sánchez,E.Müller, “Focused clustering and outlier detection in large attributed graphs.”, In: Proceedings of the 2014 ACM International Conference on Knowledge Discovery And Data Mining(SIGKDD), pp. 1346–1355, 2014.
[11] H.P.Kriegel, P. Kroger, E. Schubert, A. Zimek, “Outlier detection in arbitrarily oriented subspaces.”, In: Proceedings of the 2012 IEEE International Conference on Data Mining (ICDM), pp. 379–388, 2012.
[12] M.Anusha and J.G.R.Sathiaseelan, “An Improved K-Means Genetic Algorithm for Multi-objective Optimization”, International Journal of Applied Engineering Research,pp. 228-231, 2015.
[13] M.Anusha and J.G.R.Sathiaseelan, “An Empirical Study on Multi-Objective Genetic Algorithms using Clustering Techniques”, International Journal of Advanced Intelligence Paradigms. Vol. 8, Issue. 3, pp. 343-354, 2016.
[14] M.Anusha and J.G.R .Sathiaseelan, “Feature Selection using K-Means Genetic Algorithm for Multi-objective Optimization”, Procedia Computer Science, Vol. 57, pp. 1074-1080,Elsevier B.V., Netherland, 2015
[15] M.Anusha and J.G.R.Sathiaseelan, “An Enhanced K-means Genetic Algorithms for Optimal Clustering”, In the Proceedings of the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp.580-584, 2014.
[16] M.Anusha and J.G.R. Sathiaseelan, “Evolutionary Clustering Algorithm using Criterion-Knowledge-Ranking for Multi-objective Optimization”, Wireless Personal Communication, Springer, Vol.94, pp.2009-2030, Springer, USA. 2017.
[17] M.Anusha and J.G.R. Sathiaseelan, “Multi-objective Optimization Algorithm to the Analyses of Diabetes Disease Diagnosis”, International Journal Advanced Computer Science Application, Vol.7, pp.485-488, Thesai publishers, UK. 2016.
[18] E.Müller,M.Schiffer,T.Seidl, “Statistical selection of relevant subspace projections for outlier ranking.” In the Proceedings of 2011 IEEE International Conference on Data Engineering (ICDE),pp. 434–445, April 2011.
[19] J.Tang,Z. Chen, A. Fu, D. Cheung, “Enhancing effectiveness of outlier detections for low density patterns.” In the Proceedings of Advances in Knowledge Discovery and Data Mining, pp. 535–548 ,2002.
[20] K. Zhang, M.Hutter, H, Jin, “A new local distance-based outlier detection approach for scattered real-world data.”, In the Proceedings of Advances in Knowledge Discovery and Data Mining, pp. 813–822, 2009.
[21] F. Keller, E. Muller, K. Bohm, “HiCS: high contrast subspaces for density-based outlier ranking.” In the Proceedings of 2012 IEEE International Conference on Data Engineering (ICDE), pp. 1037–1048, 2012.
[22] A. Dukkipati, D. Ghoshdastidar, J. Krishnan, “Mixture modeling with compact support distributions for unsupervised learning”, in the Proceedings of 2016 IEEE International Joint Conference on Neural Networks(IJCNN), pp. 2706– 2713, 2016.
[23] E. Eskin, “Anomaly detection over noisy data using learned probability distributions”,In Proceedings of the International Conference on Machine Learning, Citeseer, 2000.
[24] H.-P. Kriegel, P. Kr¨oger, E. Schubert, A. Zimek, “LoOP: local outlier probabilities”, in: Proceedings of the 2009 ACM Conference on Information and Knowledge Management, pp. 1649–1652, 2009.
[25] B Sch¨olkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, R. C. Williamson, “Estimating the support of a high-dimensional distribution”, Neural Computation, Vol.13,Issue. 7, pp.1443–1471, 2001.
[26] M. El Azami, C. Lartizien, S. Canu, “Converting svdd scores into probability estimates: Application to outlier detection”, Neurocomputing, Vol. 268, pp. 64–75, 2017.
[27] J. A. Quinn, M. Sugiyama, “A least-squares approach to anomaly detection in static and sequential data”, Pattern Recognition Letters, Vol. 40,pp. 36–40,2014.
[28] M.Anusha, “Multi-Objective Optimization to Identify High Quality Clusters with Close Referential Point using Evolutionary Clustering Techniques”, Asian Journal of Computer Science and Technology, Vol.7 Issue.3, pp. 68-71, 2018.