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Titlebook: Innovations in Classification, Data Science, and Information Systems; Proceedings of the 2 Daniel Baier (Chair of Marketing and Innovation

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樓主: risky-drinking
51#
發(fā)表于 2025-3-30 12:16:37 | 只看該作者
52#
發(fā)表于 2025-3-30 15:14:34 | 只看該作者
53#
發(fā)表于 2025-3-30 17:41:35 | 只看該作者
54#
發(fā)表于 2025-3-31 00:28:01 | 只看該作者
Two-Mode Cluster Analysis via Hierarchical Bayestained. We describe the technical details of the proposed two-mode clustering methodology including its Bayesian mixture formulation and a Bayes factor heuristic for model selection. Lastly, a marketing application is provided examining consumer preferences for various brands of luxury automobiles.
55#
發(fā)表于 2025-3-31 02:56:22 | 只看該作者
ClusCorr98 - Adaptive Clustering, Multivariate Visualization, and Validation of Resultseneral validation of results of hierarchical clustering based on the adjusted Rand index is recommended. It is applied to demographical data from economics. Here the stability of each cluster can be assessed additionally.
56#
發(fā)表于 2025-3-31 07:33:47 | 只看該作者
Model-Based Cluster Analysis Applied to Flow Cytometry Datalustering, as well as the concept of cores and weighting of observations and parameters. A successful application of the method is demonstrated for a snapshot of a sample of Lake Müggelsee in Berlin (Germany).
57#
發(fā)表于 2025-3-31 11:31:29 | 只看該作者
On Stratification Using Auxiliary Variables and Discriminant Methodriance should be established. A sampling strategy using auxiliary variables data may be an alternative to simply random sampling. The method proposed below depends on the selection of a preliminary sample . of size . (. < .) and next stratification of the remainder population and selection of a stratified sample . with size ..
58#
發(fā)表于 2025-3-31 13:26:36 | 只看該作者
Measuring Distances Between Variables by Mutual Informationtities from finite datasets. The described concepts will be exemplified using large-scale gene expression data and compared to the results obtained from other measures, such as the Pearson Correlation.
59#
發(fā)表于 2025-3-31 20:43:42 | 只看該作者
Pareto Density Estimation: A Density Estimation for Knowledge Discovery in different clusters and application to high dimensional data. For high dimensional data PDE is found to be appropriate for the purpose of cluster analysis. The method is tested successfully on a difficult high dimensional real world problem: stock picking in falling markets.
60#
發(fā)表于 2025-4-1 01:23:26 | 只看該作者
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