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Titlebook: Data Analysis and Decision Support; Daniel Baier (Chair of Marketing and Innovation Ma Book 2005 Springer-Verlag Berlin Heidelberg 2005 Pl

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樓主: Malicious
51#
發(fā)表于 2025-3-30 09:45:26 | 只看該作者
52#
發(fā)表于 2025-3-30 14:43:38 | 只看該作者
Optimization in Symbolic Data Analysis: Dissimilarities, Class Centers, and Clusteringtering algorithm. Moreover, and as a first step to probabilistically based results in SDA, we consider the definition and determination of set-valued class ‘centers’ in SDA and relate them to theorems on the ‘a(chǎn)pproximation of distributions by sets’.
53#
發(fā)表于 2025-3-30 17:08:01 | 只看該作者
54#
發(fā)表于 2025-3-30 22:53:20 | 只看該作者
Fuzzy and Crisp Mahalanobis Fixed Point Clustersn) are compared to fuzzy FPCs where outliers are smoothly downweighted. An algorithm to find substantial crisp and fuzzy FPCs is proposed, the results of a simulation study and a data example are discussed.
55#
發(fā)表于 2025-3-31 02:16:26 | 只看該作者
Interpretation Aids for Multilayer Perceptron Neural Netsshare analysis and choice modeling. We suggest two different interpretation aids allowing to gain insight into predictors’ effects which both require that parameters of the MLP have already been estimated.
56#
發(fā)表于 2025-3-31 07:57:27 | 只看該作者
57#
發(fā)表于 2025-3-31 12:10:42 | 只看該作者
Performance Drivers for Depth-First Frequent Pattern Miningrst search algorithm, Eclat, and identify its performance drivers. We view Eclat as a basic algorithm and a bundle of optional algorithmic features that are taken partly from other algorithms like 1cm and Apriori, partly new ones. We evaluate the performance impact of these different features and identify the best configuration of Eclat.
58#
發(fā)表于 2025-3-31 15:46:36 | 只看該作者
59#
發(fā)表于 2025-3-31 20:13:29 | 只看該作者
60#
發(fā)表于 2025-4-1 01:17:05 | 只看該作者
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