Knowledge Discovery as Translation
Knowledge Discovery as Translation
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DOI：
10.1007/11498186_1
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
S. Ohsuga
中科院分区：
文献类型：
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作者：
S. Ohsuga
摘要本文讨论了一种将发现捕获为从非符号表示到符号表示的转换的观点。首先，讨论了符号加工与非符号加工的关系。引入了一个中间形式来表示这两者在同一框架中，并澄清了两者的区别。符号表示的特点是消除了量化度量，也抑制了元素之间的相互依赖。非符号加工具有相反的特点。因此，他们之间存在着很大的差距。本文在谓词的句法中引入了一种量度。它能够定量地测量符号和非符号表示之间的距离。这意味着，即使没有从非符号表征到符号表征的一般转换方式，当某些符号表征与给定的非符号表征没有距离或距离很小时，也是可能的。它是从数据中发现一般规律。本文在上述讨论的基础上，讨论了一种在数据库中发现隐含谓词的方法。最后，本文对相关问题进行了探讨。一个是在生成假设的过程中，另一个是数据挖掘与发现的关系。
AbstractThis 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.