Intelligent Data Engineering and Automated Learning — IDEAL by Janne Sinkkonen, Samuel Kaski (auth.), Kwong Sak Leung,

By Janne Sinkkonen, Samuel Kaski (auth.), Kwong Sak Leung, Lai-Wan Chan, Helen Meng (eds.)

X desk of Contents desk of Contents XI XII desk of Contents desk of Contents XIII XIV desk of Contents desk of Contents XV XVI desk of Contents K.S. Leung, L.-W. Chan, and H. Meng (Eds.): perfect 2000, LNCS 1983, pp. 3›8, 2000. Springer-Verlag Berlin Heidelberg 2000 four J. Sinkkonen and S. Kaski Clustering by means of Similarity in an Auxiliary house five 6 J. Sinkkonen and S. Kaski Clustering by means of Similarity in an Auxiliary house 7 0.6 1.5 0.4 1 0.2 half zero zero 10 a hundred a thousand ten thousand 10 a hundred a thousand Mutual details (bits) Mutual info (bits) eight J. Sinkkonen and S. Kaski 20 10 zero 0.1 0.3 half 0.7 Mutual info (mbits) Analyses at the Generalised Lotto-Type aggressive studying Andrew Luk St B&P Neural Investments Pty constrained, Australia summary, In generalised lotto-type aggressive studying set of rules multiple winner exist. The winners are divided right into a variety of levels (or divisions), with each one tier being rewarded in a different way. the entire losers are penalised (which could be both or differently). for you to learn some of the houses of the generalised lotto-type aggressive studying, a suite of equations, which governs its operations, is formulated. this can be then used to examine the soundness and different dynamic houses of the generalised lotto-type aggressive learning.

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Extra info for Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents: Second International Conference Shatin, N.T., Hong Kong, China, December 13–15, 2000 Proceedings

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The di erent methods for k-means calculations vary in several aspects. In all cases, the problem remains that k-means might not converge to a global optimum, depending on the selection of initial seeds. Nevertheless, from data mining and knowledge discoverly perspective, we are convinced that a pre-determinance of the number of clusters is a strict restriction. Indeed, we can obtain an optimal number of clusters heuristically by performing computations based on di erent initial settings of cluster numbers.

The combined usage of similarity and dissimilarity measures for agglomerative [11] and divisive clustering [12] of symbolic objects have been presented by Gowda and Ravi [11,12 ]. A survey of different techniques for handling symbolic data can be found in [13-18]. Most of the algorithms available in literature for clustering symbolic objects, are based on either conventional or conceptual hierarchical techniques using agglomerative or divisive methods as the core of the algorithm. In this paper, we propose a new nonhierarchical clustering scheme for symbolic objects.

Math. Statist. and Probability, 5th Berkeley, 1 (1967) 281{297. 5. Pelleg, Dan and Andrew Moore: X-means: Extending K-means with Ecient Estimation of the Number of Clusters, ICML-2000 (2000). 6. Pelleg, Dan and Andrew Moore: Accelerating Exact k -means Algorithms with Geometric Reasoning, KDD-99 (1999). 7. : Estimating the dimension of a model, Ann. , 6{2: (1978) 461464. 8. Vesanto, Juha and Johan Himberg and Esa Alhoniemi and Juha Parhankangas: Self-Organizing Map in Matlab: the SOM Toolbox, Proceedings of the Matlab DSP Conference 1999, Espoo, Finland, November (1999) 35{40.

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