Knowledge Discovery as Translation

This paper discusses a view to capture discovery as a translation from non-symbolic to symbolic representation. First, a relation between symbolic processing and non-symbolic processing is discussed. An intermediate form was introduced to represent both of them in the same framework and clarify the difference of these two. Characteristic of symbolic representation is to eliminate quantitative measure and also to inhibit mutual dependency between elements. Non-symbolic processing has opposite characteristics. Therefore there is a large gap between them. In this paper a quantitative measure is introduced in the syntax of predicate. It enables to measure the distance between symbolic and non-symbolic representations quantitatively. It means that even though there is no general way of translation from non-symbolic to symbolic representation, it is possible when there is some symbolic representation that has no or small distance from the given non-symbolic representation. It is to discover general rule from data. This paper discussed a way to discover implicative predicate in databases based on the above discussion. Finally the paper discusses some related issues. The one is on the way of generating hypothesis and the other is the relation between data mining and discovery.