Artificial Intelligence and Soft Computing: 15th by Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard

By Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, Jacek M. Zurada

The two-volume set LNAI 9692 and LNAI 9693 constitutes the refereed court cases of the fifteenth overseas convention on man made Intelligence and tender Computing, ICAISC 2016, held in Zakopane, Poland in June 2016.
The 134 revised complete papers awarded have been conscientiously reviewed and chosen from 343 submissions. The papers incorporated within the first quantity are geared up within the following topical sections: neural networks and their functions; fuzzy platforms and their purposes; evolutionary algorithms and their functions; agent platforms, robotics and regulate; and development class. the second one quantity is split within the following components: bioinformatics, biometrics and scientific functions; facts mining; synthetic intelligence in modeling and simulation; visible details coding meets computing device studying; and diverse difficulties of man-made intelligence.

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Additional resources for Artificial Intelligence and Soft Computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part II

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Efficient mining of association rules using closed itemset lattices. Inf. Syst. 24(1), 25–46 (1999) 16. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2015). R-project. org/ 17. : A comparative analysis of different categorical data clustering ensemble methods in data mining. IJCA 81(4), 46–55 (2013) 18. : Cluster ensembles - a knowledge reuse framework for combining multiple partitions. JMLR 3, 583–617 (2003) 19. : Clustering with SOM: U*C.

Partitioning Algorithm (CSPA) which is based on an overall similarity matrix S built from the membership matrix H by using S = 1r HH † , where r is the number of base clusterings. e. each time the two considered vertices are clustered together by any base clustering, their similarity is increment by 1), then a graph-based clustering method (METIS) provides the consensus clustering; (ii) HyperGraphPartitioning Algorithm (HGPA) all hyperedges as well as all vertices are equally weighted, then a hypergraph partitioning algorithm (HMETIS) defines the consensus by cutting a minimal number of edges; (iii) Meta-CLustering Algorithm (MCLA) follow the same ideas, but hyperedges weights are proportional to the similarity between vertices (instances) which is calculated using binary Jaccard measure.

Frequent Closed Patterns Based Multiple Consensus Clustering 19 Table 2. Frequent closed patterns extracted from Table 1. FCP IDs Itemsets (FCIs) 1 2 3 4 5 6 7 {P21 , P22 , P13 , {P21 , P22 , P23 , {P21 , P22 , P33 , {P11 , P12 , P13 , {P21 , P22 , P14 , {P13 } {P21 , P22 } P14 , P15 } P14 , P15 } P34 , P35 } P24 , P25 } P15 } Instance IDs {4, 5} {6, 7} {8, 9} {1, 2, 3} {4, 5, 6, 7} {1, 2, 3, 4, 5} {4, 5, 6, 7, 8, 9} number of base clusterings. , covering all dataset instances: Definition 4. , Pm } and the definition BDT = IDT ∪ PDT +1 where IDT is the instance sets of the FCPs built from DT base clusterings, and PDT +1 is the instance sets (clusters) of the previous consensus.

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