On the Use of Data Mining Tools for Data Preparation in Classification Problems

The data preparation phase is a critical step in the KDD (Knowledge Discovery in Databases) process. This phase is crucial for a good data mining result because if data is not correctly prepared, all the next phases of the process are compromised. DMPML is a framework that stores preprocessed data for different data mining algorithms in an XML document and retrieves the correct codification by the use of an XSLT document according to the needs of the data mining algorithm. This paper presents a comparison between DMPML and three data mining applications (Weka, Rapid Miner, and KNIME) that implement the directed graph approach, concerning the time spent to create and execute the data preparation tasks for two data mining algorithms. The tests were executed using different types of data sets: numerical, categorical, and mixed. We observed that the scheme used by DMPML can simplify the usage of different data mining algorithms and significantly reduce the time spent creating the data preparation tasks.