The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Unsupervised optimal fuzzy clustering
Abstract
This study reports on a method for carrying out fuzzy classification without a priori assumptions on the number of clusters in the data set. Assessment of cluster validity is based on performance measures using hypervolume and density criteria. An algorithm is derived from a combination of the fuzzy K-means algorithm and fuzzy maximum-likelihood estimation. The unsupervised fuzzy partition-optimal number of classes algorithm performs well in situations of large variability of cluster shapes, densities, and number of data points in each cluster. The algorithm was tested on different classes of simulated data, and on a real data set derived from sleep EEG signal.>
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Cites
THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS
FISHER, R. A. (1936) · Annals of Eugenics · doi:10.1111/j.1469-1809.1936.tb02137.x · Cited by 15,227
Pattern Recognition with Fuzzy Objective Function Algorithms
Bezdek, J. C. (1981) · doi:10.1007/978-1-4757-0450-1 · Cited by 9,881
Davies, D. L., & Bouldin, D. W. (1979) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1979.4766909 · Cited by 9,145
Fuzzy clustering with a fuzzy covariance matrix
Gustafson, D., & Kessel, W. (1978) · 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes · doi:10.1109/cdc.1978.268028 · Cited by 1,978
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
Zahn, C. T. (1971) · IEEE Transactions on Computers · doi:10.1109/t-c.1971.223083 · Cited by 1,759
Marr, D., & Poggio, T. (1976) · Science · doi:10.1126/science.968482 · Cited by 1,377
A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms
Bezdek, J. C. (1980) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1980.4766964 · Cited by 980
Foundations of Cyclopean Perception
Wilson, H. R., & Julesz, B. (1972) · Chicago Review · doi:10.2307/25294744 · Cited by 973
Estimating the components of a mixture of normal distributions
DAY, N. E. (1969) · Biometrika · doi:10.1093/biomet/56.3.463 · Cited by 824
Barnard, S. T., & Thompson, W. B. (1980) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1980.4767032 · Cited by 793
Cited by
A validity measure for fuzzy clustering
Xie, X. L., & Beni, G. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.85677 · Cited by 3,385
Schaeffer, S. E. (2007) · Computer Science Review · doi:10.1016/j.cosrev.2007.05.001 · Cited by 1,639
Syam, N., & Sharma, A. (2018) · Industrial Marketing Management · doi:10.1016/j.indmarman.2017.12.019 · Cited by 852
Nielsen, A. A. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.888195 · Cited by 746
Digital detection and fuzzy classification of partial discharge signals
Contin, A., Cavallini, A., Montanari, G. C., Pasini, G., & Puletti, F. (2002) · IEEE Transactions on Dielectrics and Electrical Insulation · doi:10.1109/tdei.2002.1007695 · Cited by 369
Metadata
- Authors
- Gath, I., & Geva, A. B.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 1989-07
- Volume
- 11
- Issue
- 7
- Pages
- 773-780
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2094510831
- Cited by
- 1,640
- References
- 24
- FWCI
- 7.47
- 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.
