Author(s): Jochen Hipp, Guido Lindner
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.