Rough Sets: Selected Methods and Applications in Management by Georg Peters, Pawan Lingras, Dominik Ślȩzak, Yiyu Yao

By Georg Peters, Pawan Lingras, Dominik Ślȩzak, Yiyu Yao

Rough Set conception, brought by way of Pawlak within the early Eighties, has turn into an immense a part of delicate computing in the final 25 years. even though, a lot of the point of interest has been at the theoretical figuring out of tough units, with a survey of tough units and their purposes inside of company and a lot wanted. Rough units: chosen tools and purposes in administration and Engineering presents context to tough Set concept, with every one bankruptcy exploring a real-world program of tough Sets.

Rough Sets is appropriate to managers striving to enhance their companies, researchers seeking to increase the potency in their strategies, and college researchers eager to observe tough units to real-world problems.

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IEEE Trans. Fuzzy Syst. 10, 144–154 (2002) 12. : Interval set clustering of web users with rough k-means. Technical report 2002-002, Department of Mathematics and Computer Science, St. Mary’s University, Halifax, Canada (2002) 13. : Comparison of conventional and rough k-means clustering. In: International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. LNAI, vol. 2639, pp. 130–137. Springer, Berlin (2003) 14. : Interval set clustering of web users with rough k-means. J.

Let us consider the small decision table (Table 1) depicting three diagnoses of a GP (the example is inspired by Grzymała-Busse [2]). Although the symptoms of the patients 1 and 2 are indiscernible, patient 1 suffers from flu while patient 2 does not. To indicate this ambiguity, patients with these symptoms are assigned to the upper approximations of the sets Flu=yes and Flu=no. Since the diagnosis of patient 3 is clear this patient is assigned to the lower approximation of the set Flu=no. Let us continue with our example.

These results can be used to further optimize the daily and seasonal offers in the groceries and more efficiently manage the supply chain of the company. Rough Clustering Approaches for Dynamic Environments 49 Table 2 First iteration of the dynamic rough k-means Class Cat1 Cat2 Cat3 Cat4 Cat5 1 23165 1539 519 3842 2 2651 342 77 471 562 3 16176 796 433 3012 3524 4 14779 1275 4042 1892 2565 5 58271 17815 1666 6004 6 38358 4105 15289 4466 7 86334 5811 Cat6 5619 11031 Cat7 Cat8 Cat9 889 7682 238 679 1218 5556 3304 398 195 3133 331 348 786 8641 3118 1358 11717 604 81 239 8444 3461 1430 597 87 181 3213 29445 20917 33034 3856 23620 1195 28 90 Total 10867 12749 343 986 491 7593 273 Nlower Nupper 255 583 114 9873 10396 474 Fig.

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