The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Superparamagnetic Clustering of Data
Abstract
We present a new approach for clustering, based on the physical properties of an inhomogeneous ferromagnetic model. We do not assume any structure of the underlying distribution of the data. A Potts spin is assigned to each data point and short range interactions between neighboring points are introduced. Spin-spin correlations, measured (by Monte Carlo procedure) in a superparamagnetic regime in which aligned domains appear, serve to partition the data points into clusters. Our method outperforms other algorithms for toy problems as well as for real data.
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Cites
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Statistical mechanics and phase transitions in clustering
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Cited by
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Uncovering the overlapping community structure of complex networks in nature and society
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Clustering of the self-organizing map
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Critical phenomena in complex networks
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Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering
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Metadata
- Authors
- Blatt, M., Wiseman, S., & Domany, E.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 1996-04-29
- Volume
- 76
- Issue
- 18
- Pages
- 3251-3254
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2043154233
- Cited by
- 537
- References
- 15
- FWCI
- 3.36
- 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.
