Contemporary knowledge discovery systems are mainly quantitative. This part of the research could be successfully combined with the considered qualitative research in acquisition, elicitation, and discovery of logic-based rules and patterns. The paper introduces a synthetic metamethod (SMM), which i
A method of machine discovery based on geometrical structure
β Scribed by Tsuyoshi Murata; Masamichi Shimura
- Publisher
- John Wiley and Sons
- Year
- 1998
- Tongue
- English
- Weight
- 113 KB
- Volume
- 29
- Category
- Article
- ISSN
- 0882-1666
No coin nor oath required. For personal study only.
β¦ Synopsis
In the discovery of useful theorems or laws from the data of observed instances, structures that group instances often play an important role. Acquiring data from such structure avoids the combinatorial explosion of numerous instances and enables the discovery of generalized theorems. This paper proposes a method for discovering useful theorems in the domain of plane geometry by using data from triangles, that form the basic structure in figures. We have implemented DIGEST, a discovery system based on geometrical structure, which generates figures by itself and finds theorems by using the relations among the areas of observed triangles. DIGEST succeeds in discovering new useful theorems as well as rediscovering well-known theorems such as the Menelaus Theorem and the Ceva Theorem.
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