FCM: The fuzzy c-means clustering algorithm Export

@article{BezdekJC1983, 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.}, author = {Bezdek, James C. and Ehrlich, Robert and Full, William}, citeulike-article-id = {3050014}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/0098-3004(84)90020-7}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6V7D-488G5MM-8/1/833aa10607a70c95a4ddeebfe842adde}, doi = {10.1016/0098-3004(84)90020-7}, journal = {Computers \& Geosciences}, keywords = {analysis, cluster, clustering, errors, fuzzy, least-squared, qmodel, validity}, number = {2-3}, pages = {191--203}, posted-at = {2008-07-28 13:49:02}, priority = {2}, title = {FCM: The fuzzy c-means clustering algorithm}, url = {http://dx.doi.org/10.1016/0098-3004(84)90020-7}, volume = {10}, year = {1984} }

TY - JOUR ID - BezdekJC1983 L3 - citeulike-article-id:3050014 N2 - 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. IS - 2-3 JF - Computers & Geosciences EP - 203 TI - FCM: The fuzzy c-means clustering algorithm VL - 10 SP - 191 KW - analysis KW - cluster KW - clustering KW - errors KW - fuzzy KW - least-squared KW - qmodel KW - validity AU - Bezdek, James AU - Ehrlich, Robert AU - Full, William PY - 1984/// UR - http://dx.doi.org/10.1016/0098-3004(84)90020-7 ER -