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Download e-book for iPad: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: by Andrzej Skowron, Andrzej Jankowski, Roman Swiniarski

By Andrzej Skowron, Andrzej Jankowski, Roman Swiniarski (auth.), Davide Ciucci, Masahiro Inuiguchi, Yiyu Yao, Dominik Ślęzak, Guoyin Wang (eds.)

ISBN-10: 3642412173

ISBN-13: 9783642412172

ISBN-10: 3642412181

ISBN-13: 9783642412189

This e-book constitutes the completely refereed convention lawsuits of the 14th overseas convention on tough units, Fuzzy units, info Mining and Granular Computing, RSFDGrC 2013, held in Halifax, Canada in October 2013 as one of many co-located convention of the 2013 Joint tough Set Symposium, JRS 2013. The sixty nine papers (including forty four standard and 25 brief papers) incorporated within the JRS lawsuits (LNCS 8170 and LNCS 8171) have been conscientiously reviewed and chosen from 106 submissions. The papers during this quantity conceal subject matters resembling inconsistency, incompleteness, non-determinism; fuzzy and tough hybridization; granular computing and covering-based tough units; delicate clustering; snapshot and clinical facts analysis.

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Read or Download Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 14th International Conference, RSFDGrC 2013, Halifax, NS, Canada, October 11-14, 2013. Proceedings PDF

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Download PDF by Andrzej Skowron, Andrzej Jankowski, Roman Swiniarski: Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing:

This ebook constitutes the completely refereed convention complaints of the 14th foreign convention on tough units, Fuzzy units, info Mining and Granular Computing, RSFDGrC 2013, held in Halifax, Canada in October 2013 as one of many co-located convention of the 2013 Joint tough Set Symposium, JRS 2013.

Extra resources for Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 14th International Conference, RSFDGrC 2013, Halifax, NS, Canada, October 11-14, 2013. Proceedings

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We conclude by some interesting challenges in Section 5. 2 Uncertain and Probabilistic Data and Possible Worlds We consider multidimensional objects, that is, objects of multiple attributes. An object is described by its values on those attributes. An object is certain if its value on every attribute is determined, which is the case assumed in most of the traditional data analysis methods. An object is uncertain if there exists at least one attribute where the object’s behavior can be modeled as a random variable.

This matrix is referred to in the literature as the consensus matrix. The latter expression can be reformulated thus as K E 2 = NU − ∑ ∑ ai j /Nk . k=1 i, j∈Sk This leads us to the following statement. Statement 5. , SK } is an ensemble consensus clustering if and only if it maximizes criterion K g(S) = ∑ ∑ ai j /Nk (12) k=1 i, j∈Sk where A = (ai j ) is the consensus matrix. , SK . Therefore, the iterative extraction algorithm in its partitional clusters format is applicable here. We compared the performances of this algorithm and a number of up-to-date algorithms of consensus clustering (see Table 5) [19].

1. Comparison of the accuracy of consensus clustering algorithms at WDBC dataset 4 Conclusion The paper describes least squares approximation approaches for finding individual similarity clusters which can be useful in several perspectives - summary similarity criterion, semi-average criterion, spectral clustering criterion and approximation criterion. The clustering criterion involves, in different forms, the concept of similarity threshold, or similarity shift - a value subtracted from all the similarity matrix entries.

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 14th International Conference, RSFDGrC 2013, Halifax, NS, Canada, October 11-14, 2013. Proceedings by Andrzej Skowron, Andrzej Jankowski, Roman Swiniarski (auth.), Davide Ciucci, Masahiro Inuiguchi, Yiyu Yao, Dominik Ślęzak, Guoyin Wang (eds.)


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