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Titlebook: Classification, Clustering, and Data Mining Applications; Proceedings of the M David Banks,Frederick R. McMorris,Wolfgang Gaul Conference p

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樓主: burgeon
51#
發(fā)表于 2025-3-30 11:28:08 | 只看該作者
https://doi.org/10.1057/9781137346667se objects that belong to the same class. We present some preliminary results, compared to results of other techniques, such as simulated annealing, genetic algorithms, tabu search, and k-means. Our results are as good as the best of the above methods.
52#
發(fā)表于 2025-3-30 13:18:54 | 只看該作者
53#
發(fā)表于 2025-3-30 20:34:22 | 只看該作者
Computer-Mediated Peer Response, show that spatial pyramids can converge towards geometrical pyramids. We indicate finally that spatial pyramids can give better results than Kohonen mappings and can produce a geometrical representation of conceptual lattices.
54#
發(fā)表于 2025-3-30 22:31:08 | 只看該作者
Clustering by Ant Colony Optimizationse objects that belong to the same class. We present some preliminary results, compared to results of other techniques, such as simulated annealing, genetic algorithms, tabu search, and k-means. Our results are as good as the best of the above methods.
55#
發(fā)表于 2025-3-31 03:39:16 | 只看該作者
56#
發(fā)表于 2025-3-31 06:06:55 | 只看該作者
Spatial Pyramidal Clustering Based on a Tessellation show that spatial pyramids can converge towards geometrical pyramids. We indicate finally that spatial pyramids can give better results than Kohonen mappings and can produce a geometrical representation of conceptual lattices.
57#
發(fā)表于 2025-3-31 12:04:10 | 只看該作者
Thinking Ultrametrically space. Ultrametric distance is defined from p-adic valuation. It is known that ultrametricity is a natural property of spaces that are sparse. Here we look at the quantification of ultrametricity. We also look at data compression based on a new ultrametric wavelet transform. We conclude with comput
58#
發(fā)表于 2025-3-31 13:37:14 | 只看該作者
59#
發(fā)表于 2025-3-31 21:09:16 | 只看該作者
60#
發(fā)表于 2025-4-1 01:31:11 | 只看該作者
A Dynamic Cluster Algorithm Based on , , Distances for Quantitative Datae representation of each cluster simultaneously. In its adaptive version, at each iteration of these algorithms there is a different distance for the comparison of each cluster with its representation. In this paper, we present a dynamic cluster method based on .. distances for quantitative data.
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