Abstract
In our paper, we offer an efficient Fun algorithm for discovering minimal sets of conditional attributes functionally determining a given dependent attribute, and in particular, for discovering Rough Sets certain, generalized decision, and membership distribution reducts. Fun can operate either on partitions or alternatively on stripped partitions that do not store singleton groups. It is capable of using functional dependencies occurring among conditional attributes for pruning candidate dependencies. The experimental results show that all variants of Fun have similar performance. They also prove that Fun is much faster than the Rosetta toolkit’s algorithms computing all reducts and faster than TANE, which is one of the most efficient algorithms computing all minimal functional dependencies.
Research has been supported by grant No 3 T11C 002 29 received from Polish Ministry of Education and Science.
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Kryszkiewicz, M., Lasek, P. (2007). Fast Discovery of Minimal Sets of Attributes Functionally Determining a Decision Attribute. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_34
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