Abstract
Knowledge mining is the process of deriving new and useful knowledge from vast volumes of data and patterns previously discovered and stored as background knowledge. We propose a knowledge-mining system as a repertoire of tools for discovering strong and useful patterns. A pattern is strong if it represents frequently occurring relationships. Usefulness is achieved through constraints guided by users. To be able to derive strong and useful patterns from underlying data and background knowledge we consider employing the concept of higher-order logic as a major approach of our implementation. Higher-order logic can greatly reduce the burden of programmers as it is a very high level programming scheme suitable for the development of knowledge-intensive tasks. We have shown in this paper frequent pattern mining implemented with higher-order logic. The implementation is applied to mine breast cancer data. Our design of a logic-based knowledge-mining system is intended to support higher-order and constraint mining which is the next step of our research direction.
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Kerdprasop, K., Kerdprasop, N. (2009). Knowledge Mining with a Higher-Order Logic Approach. In: Nakamatsu, K., Phillips-Wren, G., Jain, L.C., Howlett, R.J. (eds) New Advances in Intelligent Decision Technologies. Studies in Computational Intelligence, vol 199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00909-9_15
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DOI: https://doi.org/10.1007/978-3-642-00909-9_15
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