By Dominik Olszewski (auth.), Andrej Dobnikar, Uroš Lotrič, Branko à ter (eds.)
The two-volume set LNCS 6593 and 6594 constitutes the refereed complaints of the tenth foreign convention on Adaptive and average Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers awarded have been rigorously reviewed and chosen from a complete of one hundred forty four submissions. the second one quantity contains forty-one papers geared up in topical sections on trend attractiveness and studying, tender computing, structures conception, help vector machines, and bioinformatics.
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The two-volume set LNCS 6593 and 6594 constitutes the refereed complaints of the tenth foreign convention on Adaptive and common Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The eighty three revised complete papers offered have been rigorously reviewed and chosen from a complete of a hundred and forty four submissions.
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Additional info for Adaptive and Natural Computing Algorithms: 10th International Conference, ICANNGA 2011, Ljubljana, Slovenia, April 14-16, 2011, Proceedings, Part II
The EM GMM and SOMkM are faster than GSOM for approximately 10 times. Except for the data sets Ring and LetterABC, GSOM correctly ﬁnds the expected number of clusters. 4 Conclusion A novel approach of clustering Kohonen’s SOM is presented in the paper, utilizing gravitational clustering in a two-level scheme. According to the results of the experiments, the advantages of the presented method GSOM are as follows. First, GSOM is able to detect and to successfully cluster data of complex shapes with linearly non-separable regions.
KNN1 does not perform well, but the features have a strong inﬂuence on its predictions. We can conclude it overﬁts. Fig. 5. M5P successfully models dDisj and correctly predicts R = 1. The visualization shows that a single feature is responsible for the prediction, while the other two have the opposite eﬀect. Fig. 6. The neural network successfully models dXorBin and correctly predicts this instance. The explanation reveals that the ﬁrst three features are important and all three contribute towards 1.
Data Mining: Introductory and Advanced Topics. Prentice Hall, Englewood Cliﬀs (2003) 2. : Data mining: practical machine learning tools and techniques. Elsevier, Amsterdam (2005) 3. : Self-organizing maps. Springer, Heidelberg (2001) 4. : Clustering of the Self-Organizing Map. IEEE Trans. on Neural Networks 11(3), 586–600 (2000) 5. : Emergence in Self Organizing Feature Maps. In: 6th International Workshop on Self-Organizing Maps (2007) 6. : Automatic Cluster Detection in Kohonen’s SOM. IEEE Trans.