KDD-cup 99: knowledge discovery in a charitable organization's donor database
1. I N T R O D U C T I O N 2. This report describes the results of our knowledge discovery and modeling on the data of the 1997 donation campaign of an 1. American charitable organization. The two data sets (training and evaluation) contained about 95000 customers each, with an average net donation of slightly over 11 cents per customer, hence a total net donation of around $10500 results from the "mail to all" policy. The main tool we utilized for the knowledge discovery task is 2. Amdocs' Information Analysis Environment, which allows standard 2-class knowledge discovery and modeling, but also Value Weighted Analysis (VWA). In VWA, the discovered segments and models attempt to optimize the value and class membership simultaneously. Thus, our modeling was based on a 1-stage model rather than a separate analysis for donation probability and expected donation (the approach taken by all of KDD-Cup 98's reported modeling efforts except our own). 3. We concentrate the first two parts of the report on introducing the knowledge and models we have discovered. The third part deals with the methods, algorithms and comments about the results. In doing the analysis and modeling we used only the training data set of KDD-Cup 98, reserving the evaluation data set for final unbiased model evaluation for our 5 suggested models only. If our goal had been only knowledge discovery, it might have been useful to utilize the evaluation data too, especially the donors. It is probably possible to find more interesting phenomena with almost 10000 donors than with under 5000. MAIN RESULTS