Author(s)

Ö Ciftcioglu

Abstract

The term data mining refers to information elicitation. On the other hand, soft computing deals with information processing. If these two key properties can be combined in a constructive way, then this formation can effectively be used for knowledge discovery in large databases. Referring to this synergetic combination, the basic merits of data mining and soft computing paradigms are pointed out and novel data mining implementation coupled to a soft computing approach for knowledge discovery is presented. Knowledge modeling by machine learning together with the computer experiments is described and the effectiveness of the machine learning approach employed is demonstrated. 1 Introduction Although the concept of data mining can be defined in several ways, it is clear enough to understand that it is related to information extraction especially in large databases. The methods of data mining are essentially borrowed from exact sciences among which statistics play the essential role. By contrast, soft computing is essentially used for information processing by employing methods, which are capable to deal with imprecision and uncertainty especially needed in ill-defined problem areas. In the former case, the outcomes are exact within the error bounds estimated. In the latter, the outcomes are approximate and in some cases they may be interpreted as outcomes from an intelligent behavior. From these basic properties, it may be concluded that, both paradigms have their own merits and by observing these merits synergistically these paradigms can be used in a complementary way for knowledge discovery in databases. In this context, in the following two sections the properties of data mining and machine learning paradigms are pointed out. In section four the methods of soft computing are given in perspective. A novel data mining-soft computing application for

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