Titlebook: German-Japanese Interchange of Data Analysis Results; Wolfgang Gaul,Andreas Geyer-Schulz,Akinori Okada Conference proceedings 2014 Springe

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Examples in Parametric Inference with Rr and share several commonalities. By developing a conceptual link between the two approaches, we provide new insights that help to decide which of the two alternatives is to be preferred under what conditions.

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Non-additive Utility Functions: Choquet Integral Versus Weighted DNF Formulasr and share several commonalities. By developing a conceptual link between the two approaches, we provide new insights that help to decide which of the two alternatives is to be preferred under what conditions.

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Energy Deposition by X-Rays and Electrons,hree-way clustering, their algorithms are based on complicated assumptions.We propose three-mode subspace clustering based on entropy weights. The proposed algorithm excludes complicated assumptions and provides results that can be easily interpreted.

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Three-Mode Hierarchical Subspace Clustering with Noise Variables and Occasionshree-way clustering, their algorithms are based on complicated assumptions.We propose three-mode subspace clustering based on entropy weights. The proposed algorithm excludes complicated assumptions and provides results that can be easily interpreted.

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Model-Based Clustering Methods for Time Seriesumber . of clusters . each one comprising time series with a ‘similar’ structure. Classical approaches might typically proceed by first computing a dissimilarity matrix and then applying a traditional, possibly hierarchical clustering method. In contrast, here we will present a brief survey about va

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The Randomized Greedy Modularity Clustering Algorithm and the Core Groups Graph Clustering Schemeas been shown to be NP-hard, a large number of heuristic modularity maximization algorithms have been developed. In the 10th DIMACS Implementation Challenge of the Center for Discrete Mathematics & Theoretical Computer Science (DIMACS) for graph clustering our core groups graph clustering scheme com

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Comparison of Two Distribution Valued Dissimilarities and Its Application for Symbolic Clusteringts application software. We need to aggregate and then analyze those datasets. Symbolic Data Analysis (SDA) was proposed by E. Diday in 1980s (Billard L, Diday E (2007) Symboic data analysis. Wiley, Chichester), mainly targeted for large scale complex datasets. There are many researches of SDA with