The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A survey of fuzzy clustering algorithms for pattern recognition. I
Abstract
Clustering algorithms aim at modeling fuzzy (i.e., ambiguous) unlabeled patterns efficiently. Our goal is to propose a theoretical framework where the expressive power of clustering systems can be compared on the basis of a meaningful set of common functional features. Part I of this paper reviews the following issues related to clustering approaches found in the literature: relative (probabilistic) and absolute (possibilistic) fuzzy membership functions and their relationships to the Bayes rule, batch and on-line learning, prototype editing schemes, growing and pruning networks, modular network architectures, topologically perfect mapping, ecological nets and neuro-fuzziness. From this discussion an equivalence between the concepts of fuzzy clustering and soft competitive learning in clustering algorithms is proposed as a unifying framework in the comparison of clustering systems. Moreover, a set of functional attributes is selected for use as dictionary entries in the comparison of clustering algorithms, which is the subject of part II of this paper.
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Cites
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“In other words, important properties of the model must be \hard-wired or built-in, perhaps to be tuned later by experience, but not learned in any statistical meaningful way" [71].”
BackgroundA massively parallel architecture for a self-organizing neural pattern recognition machine
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Cited by
A Comprehensive Survey of Clustering Algorithms
Xu, D., & Tian, Y. (2015) · Annals of Data Science · doi:10.1007/s40745-015-0040-1 · Cited by 1,939
Support vector machines in engineering: an overview
Salcedo‐Sanz, S., Rojo‐Álvarez, J. L., Martínez‐Ramón, M., & Camps‐Valls, G. (2014) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1125 · Cited by 301
Metadata
- Authors
- Baraldi, A., & Blonda, P.
- Venue
- IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics)
- Published
- 1999
- Volume
- 29
- Issue
- 6
- Pages
- 778-785
- ISSN
- 1083-4419 (ISSN-L)
- OpenAlex
- W2167638481
- Cited by
- 529
- References
- 6
- FWCI
- 11.39
- Type
- journal article
- Language
- en
- Open access
- closed
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