The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Statistical mechanics and phase transitions in clustering
Abstract
A new approach to clustering based on statistical physics is presented. The problem is formulated as fuzzy clustering and the association probability distribution is obtained by maximizing the entropy at a given average variance. The corresponding Lagrange multiplier is related to the ``temperature'' and motivates a deterministic annealing process where the free energy is minimized at each temperature. Critical temperatures are derived for phase transitions when existing clusters split. It is a hierarchical clustering estimating the most probable cluster parameters at various average variances.
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Cites
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Cited by
'Neural-gas' network for vector quantization and its application to time-series prediction
Martinetz, T. M., Berkovich, S. G., & Schulten, K. J. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.238311 · Cited by 1,431
Cluster analysis for gene expression data: a survey
Jiang, D., Tang, C., & Zhang, A. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.68 · Cited by 1,235
Distributional clustering of English words
Pereira, F., Tishby, N., & Lee, L. (1993) · Proceedings of the 31st annual meeting on Association for Computational Linguistics - · doi:10.3115/981574.981598 · Cited by 974
Elements of Artificial Neural Networks
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Predicting slow structural transitions in macromolecular systems: Conformational flooding
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Metadata
- Authors
- Rose, K., Gurewitz, E., & Fox, G. C.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 1990-08-20
- Volume
- 65
- Issue
- 8
- Pages
- 945-948
- ISSN
- 0031-9007 (ISSN-L)
- OpenAlex
- W2025887562
- Cited by
- 458
- References
- 12
- FWCI
- 5.94
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
