FCM: The fuzzy c-means clustering algorithm. Bezdek, J. C., Ehrlich, R., & Full, W. Computers & Geosciences, 10(2):191–203, January, 1984. ![FCM: The fuzzy c-means clustering algorithm [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering criterion used to aggregate subsets is a generalized least-squares objective function. Features of this program include a choice of three norms (Euclidean, Diagonal, or Mahalonobis), an adjustable weighting factor that essentially controls sensitivity to noise, acceptance of variable numbers of clusters, and outputs that include several measures of cluster validity.
@article{bezdek_fcm_1984,
title = {{FCM}: {The} fuzzy c-means clustering algorithm},
volume = {10},
issn = {0098-3004},
shorttitle = {{FCM}},
url = {https://www.sciencedirect.com/science/article/pii/0098300484900207},
doi = {10.1016/0098-3004(84)90020-7},
abstract = {This paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering criterion used to aggregate subsets is a generalized least-squares objective function. Features of this program include a choice of three norms (Euclidean, Diagonal, or Mahalonobis), an adjustable weighting factor that essentially controls sensitivity to noise, acceptance of variable numbers of clusters, and outputs that include several measures of cluster validity.},
language = {en},
number = {2},
urldate = {2021-10-01},
journal = {Computers \& Geosciences},
author = {Bezdek, James C. and Ehrlich, Robert and Full, William},
month = jan,
year = {1984},
keywords = {Cluster analysis, Cluster validity, Fuzzy QMODEL, Fuzzy clustering, Least-squared errors},
pages = {191--203},
}
Downloads: 0
{"_id":"2vt6D9bifQPpiokqp","bibbaseid":"bezdek-ehrlich-full-fcmthefuzzycmeansclusteringalgorithm-1984","downloads":0,"creationDate":"2018-01-22T16:01:02.033Z","title":"FCM: The fuzzy c-means clustering algorithm","author_short":["Bezdek, J. C.","Ehrlich, R.","Full, W."],"year":1984,"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","bibdata":{"bibtype":"article","type":"article","title":"FCM: The fuzzy c-means clustering algorithm","volume":"10","issn":"0098-3004","shorttitle":"FCM","url":"https://www.sciencedirect.com/science/article/pii/0098300484900207","doi":"10.1016/0098-3004(84)90020-7","abstract":"This paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering criterion used to aggregate subsets is a generalized least-squares objective function. Features of this program include a choice of three norms (Euclidean, Diagonal, or Mahalonobis), an adjustable weighting factor that essentially controls sensitivity to noise, acceptance of variable numbers of clusters, and outputs that include several measures of cluster validity.","language":"en","number":"2","urldate":"2021-10-01","journal":"Computers & Geosciences","author":[{"propositions":[],"lastnames":["Bezdek"],"firstnames":["James","C."],"suffixes":[]},{"propositions":[],"lastnames":["Ehrlich"],"firstnames":["Robert"],"suffixes":[]},{"propositions":[],"lastnames":["Full"],"firstnames":["William"],"suffixes":[]}],"month":"January","year":"1984","keywords":"Cluster analysis, Cluster validity, Fuzzy QMODEL, Fuzzy clustering, Least-squared errors","pages":"191–203","bibtex":"@article{bezdek_fcm_1984,\n\ttitle = {{FCM}: {The} fuzzy c-means clustering algorithm},\n\tvolume = {10},\n\tissn = {0098-3004},\n\tshorttitle = {{FCM}},\n\turl = {https://www.sciencedirect.com/science/article/pii/0098300484900207},\n\tdoi = {10.1016/0098-3004(84)90020-7},\n\tabstract = {This paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering criterion used to aggregate subsets is a generalized least-squares objective function. Features of this program include a choice of three norms (Euclidean, Diagonal, or Mahalonobis), an adjustable weighting factor that essentially controls sensitivity to noise, acceptance of variable numbers of clusters, and outputs that include several measures of cluster validity.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2021-10-01},\n\tjournal = {Computers \\& Geosciences},\n\tauthor = {Bezdek, James C. and Ehrlich, Robert and Full, William},\n\tmonth = jan,\n\tyear = {1984},\n\tkeywords = {Cluster analysis, Cluster validity, Fuzzy QMODEL, Fuzzy clustering, Least-squared errors},\n\tpages = {191--203},\n}\n\n\n\n","author_short":["Bezdek, J. C.","Ehrlich, R.","Full, W."],"key":"bezdek_fcm_1984","id":"bezdek_fcm_1984","bibbaseid":"bezdek-ehrlich-full-fcmthefuzzycmeansclusteringalgorithm-1984","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/0098300484900207"},"keyword":["Cluster analysis","Cluster validity","Fuzzy QMODEL","Fuzzy clustering","Least-squared errors"],"metadata":{"authorlinks":{}},"downloads":0,"html":""},"search_terms":["fcm","fuzzy","means","clustering","algorithm","bezdek","ehrlich","full"],"keywords":["cluster analysis","cluster validity","fuzzy qmodel","fuzzy clustering","least-squared errors"],"authorIDs":[],"dataSources":["9cexBw6hrwgyZphZZ","iwKepCrWBps7ojhDx"]}