Outlier Detection and Data Cleaning in Multivariate Non-Normal Samples: The PAELLA Algorithm |
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Authors: | Manuel Castejón Limas Joaquín B. Ordieres Meré Francisco J. Martínez de Pisón Ascacibar Eliseo P. Vergara González |
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Affiliation: | (1) Dept. Ingeniería Eléctrica, Universidad de León, Leóon, Spain;(2) Dept. Ingeniería Mecánica, Universidad de La Rioja, Logroño, Spain |
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Abstract: | A new method of outlier detection and data cleaning for both normal and non-normal multivariate data sets is proposed. It is based on an iterated local fit without a priori metric assumptions. We propose a new approach supported by finite mixture clustering which provides good results with large data sets. A multi-step structure, consisting of three phases, is developed. The importance of outlier detection in industrial modeling for open-loop control prediction is also described. The described algorithm gives good results both in simulations runs with artificial data sets and with experimental data sets recorded in a rubber factory. Finally, some discussion about this methodology is exposed. |
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Keywords: | outlier multivariate non-normal data cleaning EM algorithm cluster analysis mixture model |
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