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Analysing Warranty Claims of Automobiles

An Application Description Following the CRISP-DM Data Mining Process

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Internet Applications (ICSC 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1749))

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Abstract

This paper describes a real world application of Data Mining methods for deviation detection. The goal is to analyse warranty claims in the automobile sector. Basically we want to support the technical engineers concerned with warranty issues in two ways: First of all we want to guide them during verification of their hypothesis and additionally we want to strengthen their creative and inspirational potentials.

For this purpose we accessed the Quality Information System (QUIS) of DaimlerChrysler. The whole project was carried through according to the CRISP-DM data mining process. The methods from Data Mining that we applied were: baysian nets, boolean association rules, generalised association rules, quantitative association rules and sequential patterns.

We present some of the data mining results exemplarily, discuss the difficulties we encountered and finally give a short conclusion.

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© 1999 Springer-Verlag Berlin Heidelberg

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Hipp, J., Lindner, G. (1999). Analysing Warranty Claims of Automobiles. In: Hui, L.C.K., Lee, DL. (eds) Internet Applications. ICSC 1999. Lecture Notes in Computer Science, vol 1749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-46652-9_4

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  • DOI: https://doi.org/10.1007/978-3-540-46652-9_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66903-6

  • Online ISBN: 978-3-540-46652-9

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