Clustering Analysis of Maximal Tree Method and Fuzzy C-Means Algorithm
LV Bing-chao · 2003
Clustering results of fuzzy c-means algorithm and maximal tree method in common use of project are compared, similitude and difference of their clustering results are analyzed from algorithms' own angle. It is testified through simulation that maximal tree method is comparatively capable of small sample sets; and fuzzy c-means algorithms is not only capable of small sample sets, but also capable of multidimensional large sample sets of corps shape and discrepancy not too in sample number of every class and overlapping data among classes, still convenient for computer program realizing.