By Da Ruan, Javier Montero, Jie Lu, Luis Martinez, Pierre D'Hondt, Etienne E. Kerre

FLINS, initially an acronym for Fuzzy common sense and clever applied sciences in Nuclear technology, is now prolonged to Computational Intelligence for utilized examine. The contributions to the 8th ebook within the sequence of FLINS meetings conceal cutting-edge learn, improvement, and know-how for computational intelligence platforms often, and for clever determination and keep watch over particularly.

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**Extra info for Computational Intelligence in Decision and Control: Proceedings of the 8th International FLINS Conference, Madrid, Spain, 21-24 September 2008 (World Scientific ... Engineering and Information Science)**

**Sample text**

The multivariate data space dom(ad. , D ~ V. We consider two types of attribute, numerical and categorical. A numerical attribute has an inherent order defined over its domain, and the operations allowed are equality, inequality, 'greater than', 'less than', addition, subtraction, multiplication and division. A categorical attribute has no order defined over its domain, and the only operations allowed are equality and inequality. For simplicity, we assume the domain of any attribute is finite unless otherwise stated and, in the case of numerical attributes, we assume their domains are discretized and then represented as natural numbers.

For a multivariate data space the frame of discernment can be taken to be the set of all hypertuples. For a given sample of the data space, a mass function can be derived such that for each hypertuple its mass is proportional to the number of data items that are covered by the hypertuple. For two mass functions thus derived, combining them into a new mass function appears trivial. However this approach leads to some interesting questions whose answers have the potential to lay the foundation for a completely new rule of combination.

O. Boudraa, A. Bentabet, F. Salzenstein and L. Guillon, Electronic Letters on Computer Vision and Image Analysis 4, 1 (2004). 10. T. Denoeux, IEEE Transactions on Systems, Man and Cybernetics: 25, 804 (1995). uk) 5Shenyang Television University, ShenYang, LiaoNing, PRChina, 110003 In this paper, we improve a very famous algorithm Apriori for data mining. , maximal frequent patterns or itemsets. , A < B < C < D < for items A,B,C,D etc. Furthermore, items within an itemset are sorted in lexicograghic order.