Rigor data mining (DM) research has successfully developed advanced data mining techniques and algorithms, and many organizations have great expectations to take more benefit of their vast data warehouses in decision making. Even when there are some success stories the current status in practice is mainly including great expectations that have not yet been fulfilled. DM researchers have recently become interested in utility-based DM (UBDM) starting to consider some of the economic utility factors (like cost of data, cost of measurement, cost of class label and so forth), but yet many other utility factors are left outside the main directions of UBDM. The goal of this position paper is (1) to motivate researchers to consider utility from broader perspective than usually done in UBDM context and (2) to introduce a new generic framework for these broader utility considerations in DM research. Besides describing our multi-criteria utility based framework (MCUF) we present a few hypothetical examples showing how the framework might be used to consider utilities of some potential DM research stakeholders.
|Title of host publication||Data Mining for Business Applications|
|Editors||C. Soares, R. Ghani|
|Place of Publication||Amsterdam|
|Publication status||Published - 2010|
|Name||Frontiers in Artificial Intelligence and Applications|
Puuronen, S., & Pechenizkiy, M. (2010). Towards the generic framework for utility considerations in data mining research. In C. Soares, & R. Ghani (Eds.), Data Mining for Business Applications (pp. 49-65). (Frontiers in Artificial Intelligence and Applications; Vol. 218). IOS Press. https://doi.org/10.3233/978-1-60750-633-1-49