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Outlier Effects on Databases

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Advances in Information Systems (ADVIS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3261))

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Abstract

Real data and databases always contain some kind of heterogenity or contamination, which is called “outliers”. Outliers are defined as the few observations or records which appear to be inconsistent with the remainder group of the sample and more effective on prediction values. Isolated outliers may also have positive impact on the results of data analysis and data mining. In this study, we are concerned with outliers in time series which have two special cases, innovational outlier (IO) and additive outlier (AO). The occurence of AO indicates that action is required, possibly to adjust the measuring instrument or at least to print an error message on the database. However, if IO occurs, no adjustment of the measurement operation is required. At the end of the study, the results of the simulation and variance analysis on the produced data sets are emphasized.

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Author information

Authors and Affiliations

  1. Tire Kutsan Post, Secondary Vocational School, Ege University, 35900, Tire, İzmir, Turkey

    Ahmet Kaya

Authors
  1. Ahmet Kaya
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Editor information

Editors and Affiliations

  1. Dokuz Eylül University, lzmir, Turkey

    Tatyana Yakhno

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© 2004 Springer-Verlag Berlin Heidelberg

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Kaya, A. (2004). Outlier Effects on Databases. In: Yakhno, T. (eds) Advances in Information Systems. ADVIS 2004. Lecture Notes in Computer Science, vol 3261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30198-1_10

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  • DOI: https://doi.org/10.1007/978-3-540-30198-1_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23478-4

  • Online ISBN: 978-3-540-30198-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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