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Titlebook: Between Data Science and Applied Data Analysis; Proceedings of the 2 Martin Schader,Wolfgang Gaul,Maurizio Vichi Conference proceedings 200

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樓主: dentin
11#
發(fā)表于 2025-3-23 13:39:44 | 只看該作者
Amit Kumar Tyagi,Niladhuri Sreenath. By using the general theory of ‘convexity-based clustering criteria’ (., ., .) we derive a k-means-like clustering algorithm that uses ‘maximum support-plane partitions’ (in terms of likelihood ratio vectors) in the same way as classical SSQ clustering uses ‘minimum-distance partitions’.
12#
發(fā)表于 2025-3-23 16:09:37 | 只看該作者
13#
發(fā)表于 2025-3-23 21:01:34 | 只看該作者
Core-Based Clustering Techniques Then a more general . approach based on pair-wise distances is recommended. Simulation studies are carried out in order to compare the new clustering techniques with the well-known ones. Moreover, a successful application is presented. Here the task is to discover clusters with quite different number of observations in a high-dimensional space.
14#
發(fā)表于 2025-3-23 22:54:16 | 只看該作者
15#
發(fā)表于 2025-3-24 02:32:19 | 只看該作者
Discriminant Analysis With Categorical Variables: A Biplot Based Approach predictors is discussed. Specific attention is devoted to what is termed a ‘reversal’ when dealing with two binary (categorical) predictor variables. A proposal using biplot methodology is made for dealing with this problem.
16#
發(fā)表于 2025-3-24 07:14:49 | 只看該作者
Efficient Density Clustering Using Basin Spanning Treesum, and the trees can be used for simplified representation and visualization of the observations. We compare the accuracy and speed of different approximations, apply the method to real-world data sets and compare its computational complexity to published algorithms.
17#
發(fā)表于 2025-3-24 12:42:22 | 只看該作者
18#
發(fā)表于 2025-3-24 15:39:44 | 只看該作者
Two Approaches for Discriminant Partial Least SquaresLS proposed by (.) but used in the discrimination context. The second proposal, in the same framework, leads to consider the PLS Redundancy Analysis proposed by (.) by using suitable metrics. Some examples of data treatment are given.
19#
發(fā)表于 2025-3-24 21:40:45 | 只看該作者
20#
發(fā)表于 2025-3-25 01:37:34 | 只看該作者
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