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| Reginald E. Hammah, John H. Curran, "Validity Measures for the Fuzzy Cluster Analysis of Orientations," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1467-1472, December, 2000. | |||
| BibTex | x | ||
| @article{ 10.1109/34.895981, author = {Reginald E. Hammah and John H. Curran}, title = {Validity Measures for the Fuzzy Cluster Analysis of Orientations}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {22}, number = {12}, issn = {0162-8828}, year = {2000}, pages = {1467-1472}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.895981}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Pattern Analysis and Machine Intelligence TI - Validity Measures for the Fuzzy Cluster Analysis of Orientations IS - 12 SN - 0162-8828 SP1467 EP1472 EPD - 1467-1472 A1 - Reginald E. Hammah, A1 - John H. Curran, PY - 2000 KW - Fuzzy cluster analysis KW - cluster analysis KW - cluster validity index KW - cluster performance measure KW - cluster validity indices KW - cluster performance measures KW - discontinuities KW - orientations KW - spherical data KW - directional data. VL - 22 JA - IEEE Transactions on Pattern Analysis and Machine Intelligence ER - | |||
Abstract—Fuzzy K-means clustering can be applied to the automatic identification of sets in discontinuity data after suitable adaptation of the algorithm. To establish the number of clusters in a data set, modified versions of the validity measures of Gath and Geva, Xie-Beni and Fukuyama-Sugeno are presented in this paper.
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