Abstract
Nowadays society confronts to a huge volume of information which has to be transformed into knowledge. One of the most relevant aspect of the knowledge extraction is the detection of outliers. Numerous algorithms have been proposed with this purpose. However, not all of them are suitable to deal with very large data sets. In this work, a new approach aimed to detect outliers in very large data sets with a limited execution time is presented. This algorithm visualizes the tuples as N-dimensional particles able to create a potential well around them. Later, the potential created by all the particles is used to discriminate the outliers from the objects composing clusters. Besides, the capacity to be parallelized has been a key point in the design of this algorithm. In this proof-of-concept, the algorithm is tested by using sequential and parallel implementations. The results demonstrate that the algorithm is able to process large data sets with an affordable execution time, so that it overcomes the curse of dimensionality.
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References
Corchado, E., Wozniak, M., Abraham, A., de Carvalho, A.C.P.L.F., Snásel, V.: Recent trends in intelligent data analysis. Neurocomputing 126, 1–2 (2014)
Abraham, A.: Special issue: Hybrid approaches for approximate reasoning. Journal of Intelligent and Fuzzy Systems 23(2-3), 41–42 (2012)
Aggarwal, C.C.: Outlier Analysis. Springer (2013)
Peteiro-Barral, D., Guijarro-Berdiñas, B.: A survey of methods for distributed machine learning. Progress in AI 2(1), 1–11 (2013)
Johnson, T., Kwok, I., Ng, R.T.: Fast computation of 2-dimensional depth contours. In: Agrawal, R., Stolorz, P.E., Piatetsky-Shapiro, G. (eds.) Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD 1998), pp. 224–228. AAAI Press (1998)
Struyf, A., Rousseeuw, P.J.: High-dimensional computation of the deepest location. Computational Statistics and Data Analysis 34, 415–426 (1999)
Rajaraman, A., Ullman, J.D.: Mining of massive datasets. Cambridge University Press, Cambridge (2012)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann (2000)
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© 2014 Springer International Publishing Switzerland
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Cárdenas-Montes, M. (2014). Depth-Based Outlier Detection Algorithm. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_11
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DOI: https://doi.org/10.1007/978-3-319-07617-1_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07616-4
Online ISBN: 978-3-319-07617-1
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