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 synthetic Intelligence and gentle Computing, ICAISC 2016, held in Zakopane, Poland in June 2016.
The 134 revised complete papers awarded have been rigorously 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 structures and their purposes; evolutionary algorithms and their functions; agent structures, robotics and keep an eye on; and trend class. the second one quantity is split within the following elements: bioinformatics, biometrics and clinical functions; info mining; synthetic intelligence in modeling and simulation; visible info coding meets desktop studying; and numerous difficulties of synthetic intelligence.
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Additional info for Artificial Intelligence and Soft Computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part II
Clark et al. Table 2. 65 For every data set a set of templates was created. Templates were formed by replacing incrementally (with 5 % increment) existing speciﬁed attribute values by lost values. , until at least one entire row of the data sets was full of lost values.
New Phytol. 11(2), 37–50 (1912). x 11. : Weighted consensus clustering. In: Proceedings of the SIAM Conference on Data Mining, pp. 798–809 (2008) 12. : UCI machine learning repository (2013). edu/ ml 26 A. Al-Najdi et al. 13. : A new approach for association rule mining and bi-clustering using formal concept analysis. In: Perner, P. ) MLDM 2012. LNCS, vol. 7376, pp. 86–101. Springer, Heidelberg (2012) 14. : UCI repository of machine learning databases (1998). html 15. : Eﬃcient mining of association rules using closed itemset lattices.
1007/978-3-319-39384-1 2 Frequent Closed Patterns Based Multiple Consensus Clustering 15 Rather than depending on validation measures, another approach is to combine the multiple clustering solutions generated by several clustering algorithms and/or settings, in order to produce a ﬁnal clustering which is better than each individual algorithm can produce. This technique is called consensus clustering, aggregation of clusterings or ensemble clustering, and the clustering algorithms to be combined are called base clustering algorithms.