By A. Klose, A. Nürnberger, D. Nauck, R. Kruse (auth.), Dr. Abraham Kandel, Dr. Mark Last, Dr. Horst Bunke (eds.)
The quantity deals a entire insurance of the new advances within the program of sentimental computing and fuzzy common sense idea to info mining and information discovery databases. It makes a speciality of the various toughest, and but unsolved, problems with information mining like understandability of styles, discovering complicated relationships among attributes, dealing with lacking and noisy info, mining very huge datasets, switch detection in time sequence, and integration of the invention technique with database administration systems.
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Additional info for Data Mining and Computational Intelligence
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327-358. 18. , and Mikut, R (1999). Automatic generation and evaluation of interpretable rule bases for fuzzy systems. In Computational Intelligence for Modelling, Control and Automation CIMCA'99, pages 192197. lOS Press, Amsterdam. 19. , and Kruse, R (1994). Modifications of Genetic Algorithms for Designing and Optimizing Fuzzy Controllers, In: Proc. IEEE Conference on Evolutionary Computation, pp. 28-33, IEEE, Orlando, FL. 20. , and R. Kruse (1997). Constructing a Fuzzy Controller from Data. Fuzzy Sets and Systems, 85:177-193.
This is a very fast procedure and requires two cycles through the training set (numerical attributes only). In the first cycle, all antecedents are identified, and in the second cycle, the best consequent for each antecedent is determined and performances values for the rules are computed. u;1) (Xl)' ... u;n) (Xn)} x, 22 we denote the degree of fulfillment of a rule given input pattern p. The consequent is a class label Cr. Let class (p) denote the class of p. The performance of a rule Rr = (J-ln cr) is defined as perfr = I~I L~r(P) .