PhD Thesis Euler
Knowledge Discovery in Databases (KDD) is a nontrivial process centered around one or more applications of a Machine Learning algorithm to real world data. Steps leading towards this central step prepare the examples from which the algorithm learns, and thus create the example representation language. Steps following the central step may deploy the learned results to new data. In this thesis, the complete process is described from a conceptual view, and the MiningMart software is presented which supports the whole process, but puts its focus on data preparation for KDD. This preparation phase is the most time-consuming part of the process, and is comprehensively supported in new ways by the contributions towards MiningMart made in this thesis. The result are greatly reduced user efforts for rapid prototyping, modelling, execution, publication and re-use of KDD processes.
