On Operations Research and Statistics Techniques: Keys to Quantitative Data Mining
Jorge Luis Romeu · American Journal of Mathematical and Management Sciences · 2006
SYNOPTIC ABSTRACTWith the current information explosion, both Knowledge Discovery in Data Bases (KDD) and Data Mining (DM), one of its most important phases, are becoming ubiquitous. Hence, there is an increasing need for training professionals to work as KDD/DM analysts. On the other hand, statisticians and operations researchers (O.R) combine three skills widely used in KDD/DM: computer programming, systems optimization and data analysis techniques. This document critically overviews the main applications of statistics and O.R. to the quantitative aspects of KDD/DM and presents an illustrative, real life example. Its purposes are, first to alert statisticians and O.R. professionals about the challenges and opportunities that, with little extra training await them in the field of KDD/DM, giving further references about where to learn in-depth about them. Secondly, to provide other KDD professionals, of backgrounds different from statistics and O.R., a clearer picture about the capabilities statisticians and O.R. researchers bring to the DM analysis area, and about how these can help enhance KDD/DM work, therefore enhancing the interface between the very diverse KDD/DM team members.