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Extra info for Computational aspects of theory of elliptic curves
By adaptive tuning of c, one is likely to obtain interesting results via appropriate heuristics. The reader is referred to [BazanOO] for a study of a possible design of such heuristics aimed at extraction of approximate association rules from data. Approximate reducts based on conditional frequency The idea of finding reducts based on a global discernibility measure may be refined yet: one may search for reducts preserving a given frequency characteristic of decision. To have a glimpse into this venue of research, let us CHAPTER 1.
1\ Dk, where Di = (ai E Wi), is characterized by the following parameters: 1. length(T), which is the number of descriptors occurring in T. 2. support(T) = supportu(T), which is the number of objects in U satisfying T. 3. applength(T), which is Ll:Si:'Sk Volu~e(D;) called the approximated length of the generalized template T. Functions applength and length coincide for simple templates. The complexity and algorithmic problems related to search for good templates with applications are given a throughout examination in [Nguyen Sinh HoaOO] to which the reader is invited for details.
By an appropriate application of Boolean reasoning (cf. Skowron and Rauszer (op. )). 25) The matrix Mtd is the relative discernibility matrix of Ad. From the relative discernibility matrix, the relative discernibility function ftd is constructed in the same way as the discernibility function was constructed from the discernibility matrix. Again, as shown by Skowron and Rauszer (op. ), the set PRJ- IMP(ftJ of prime implicants of ftd and the set REL- RED(Ad) of relative reducts of Ad are in one-to-one correspondence described above.