Using projection-based clustering to find distance- and density-based clusters in high-dimensional data
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Publication:2236772
DOI10.1007/s00357-020-09373-2OpenAlexW3080491161MaRDI QIDQ2236772
Michael C. Thrun, Alfred Ultsch
Publication date: 26 October 2021
Published in: Journal of Classification (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00357-020-09373-2
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Cites Work
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- Clustering by Passing Messages Between Data Points
- A note on two problems in connexion with graphs
- Swarm intelligence for self-organized clustering
- An examination of indexes for determining the number of clusters in binary data sets
- New approaches in classification and data analysis
- Factorial and reduced \(K\)-means reconsidered
- Initializing \(K\)-means batch clustering: A critical evaluation of several techniques
- Advances in self-organizing maps and learning vector quantization. Proceedings of the 11th international workshop WSOM 2016, Houston, TX, USA, January 6--8, 2016
- Multidimensional scaling. I: Theory and method
- Cluster Analysis
- On Using Principal Components Before Separating a Mixture of Two Multivariate Normal Distributions
- Extensions of Lipschitz mappings into a Hilbert space
- Multisurface method of pattern separation for medical diagnosis applied to breast cytology.
- Finding Groups in Data
- An efficient algorithm for a complete link method
- Model-Based Clustering, Discriminant Analysis, and Density Estimation
- An elementary proof of a theorem of Johnson and Lindenstrauss
- Pareto Density Estimation: A Density Estimation for Knowledge Discovery
- Computer Programs for Hierarchical Polythetic Classification ("Similarity Analyses")
- On Some Clustering Techniques
- Sur la liaison et la division des points d'un ensemble fini
- Factorial \(k\)-means analysis for two-way data.