By Vicenç Torra, Anders Dahlbom, Yasuo Narukawa
This publication is devoted to Prof. Sadaaki Miyamoto and offers state-of-the-art papers in a number of the components during which he contributed. Bringing jointly contributions by means of prime researchers within the box, it concretely addresses clustering, multisets, tough units and fuzzy units, in addition to their functions in components equivalent to decision-making.
The ebook is split in 4 elements, the 1st of which makes a speciality of clustering and type. the second one half places the highlight on multisets, luggage, fuzzy luggage and different fuzzy extensions, whereas the 3rd bargains with tough units. Rounding out the assurance, the final half explores fuzzy units and decision-making.
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589–594, 1990. 25. S. Oshio, K. Honda, S. Ubukata and A. Notsu, “Deterministic annealing framework in MMMsinduced fuzzy co-clustering and its applicability,” International Journal of Computer Science and Network Security, vol. 16, no. 1, pp. 43–50, 2016. 26. K. Sjölander, K. Karplus, M. Brown, R. Hughey, A. Krogh, I. Saira Mian and D. Haussler, “Dirichlet mixtures: a method for improved detection of weak but significant protein sequence homology,” Computer Applications in the Biosciences, vol. 12, no.
We then obtain the desired clustering results, as shown in Fig. 4 using Algorithm 6. Thus, this example shows that on data that causes Algorithm 3 to fail, the proposed algorithm (Algorithm 6) achieves a successful clustering result with the help of pairwise constraints given by a supervisor. Although we tested Algorithm 6 58 Y. 5. 3 Example for Algorithm 7 In this example, we apply Algorithm 7 to the data in Fig. 1 with fewer must-link and cannot-link constraints than shown in Fig. 3. First, we see that Algorithm 5 fails with the given constraints as follows.
Of the 4th IEEE Intern. Conf. on Fuzzy Systems (FUZZ-IEEE/IFES’95), Yokohama, Japan, March 20–24, 1995, pp. 2227–2232, 1995. 24. B. MacQueen, Some methods of classification and analysis of multivariate observations, Proc. of 5th Berkeley Symposium on Math. Stat. , pp. 281–297, 1967. 25. G. McLachlan, D. Peel, Finite Mixture Models, Wiley, New York, 2000. 26. S. Miyamoto, Fuzzy Sets in Information Retrieval and Cluster Analysis, Kluwer, Dordrecht, 1990. 27. S. Miyamoto, M. Mukaidono, Fuzzy c-means as a regularization and maximum entropy approach, Proc.