By Yager R.R.
We examine the problem of database mining.An strategy is advised dependent uppon using fuzzy lingusitic summaries.
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Extra resources for Fuzzy Summaries in Database mining
The loan company application is described in more detail by Michie (1989), the oil slick detector is from Kubat et al. (1998), the electric load forecasting work is by Jabbour et al. (1988), and the application to preventative maintenance of electromechanical devices is from Saitta and Neri (1998). 3 (including the figures of dollars saved and related literature references) appear at the websites of the Alberta Ingenuity Centre for Machine Learning and MLnet, a European network for machine learning.
Each case concerns one contract, and the outcome is whether the contract is deemed acceptable or unacceptable. The acceptable contracts are ones in which agreements were accepted by both labor and management. The unacceptable ones are either known offers that fell through because one party would not accept them or acceptable contracts that had been significantly perturbed to the extent that, in the view of experts, they would not have been accepted. There are 40 examples in the dataset (plus another 17 that are normally reserved for test purposes).
However, we’d hardly be interested in sets that contained a very large number of rules. In fact, we’d hardly be interested in sets that had more rules than there are examples because it is difficult to imagine needing more than one rule for each example. So if we were to restrict consideration to rule sets smaller than that, the problem would be substantially reduced, although still very large. 3 because these rules contain numbers. If they are real numbers, you can’t enumerate them, even in principle.