Brumen, Boštjan ; Welzer-Družovec, Tatjana ; Jaakkola, Hannu
Data mining has received a large amount of attention lately. The fact that data hide important knowledge is a primary motivating factor. Most of the research is focusing on data mining in "large" ...databases. "Small" databases have not received much attention since they are owned by companies who can not afford expensive processes. The identified problem in our paper is the prediction of costs connected to the collection of data to be used in the data mining algorithm. We present the idea of an additive approach to the problem. The additive approach takes small sample sizes at early stages of data mining when the costs are still low compared to the costs at the later stages when more and more data are required, and based on small sample sizes tries to make a prediction, how much would the total costs be for a given (large) sample size.
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