文件列表:

MinMax_k-Means_software (0, 2019-03-31)
MinMax_k-Means_software\sqdist.m (265, 2007-09-17)
__MACOSX (0, 2019-03-31)
__MACOSX\MinMax_k-Means_software (0, 2019-03-31)
__MACOSX\MinMax_k-Means_software\._sqdist.m (274, 2007-09-17)
MinMax_k-Means_software\Coil2.mat (231381, 2012-10-02)
__MACOSX\MinMax_k-Means_software\._Coil2.mat (218, 2012-10-02)
MinMax_k-Means_software\contrast.m (913, 2019-03-31)
__MACOSX\MinMax_k-Means_software\._contrast.m (266, 2019-03-31)
MinMax_k-Means_software\demo.m (2011, 2019-03-31)
__MACOSX\MinMax_k-Means_software\._demo.m (274, 2019-03-31)
MinMax_k-Means_software\MinMax_kmeans.m (7653, 2019-03-31)
__MACOSX\MinMax_k-Means_software\._MinMax_kmeans.m (274, 2019-03-31)
__MACOSX\._MinMax_k-Means_software (182, 2019-03-31)

This software contains the implementation of the the MinMax k-means algorithm, described in G.Tzortzis and A.Likas, "The MinMax k-means Clustering Algorithm", Pattern Recognition, 2014. The main routine can be found in the MinMax_kmeans.m file. Type "help MinMax_kmeans" within MatLab for usage details. Moreover, a demo file (demo.m) is included that shows how to call the MinMax k-means function. The Coil2 dataset from the paper is used for the demonstration. It contains three of the Coil-20 objects (object 15, 18 and 19) represented by 1000 visual words (Coil2.mat). For any questions regarding the implementation do not hesitate to contact me at: gtzortzi@cs.uoi.gr G. Tzortzis

近期下载者：

相关文件：

评论：[我要评论] [举报此文件]

收藏者：