程式碼及說明

FCM
fcmtest.m	Find center of Fuzzy C-means clustering with 2 dimensions datasets.
fcm.m	Find clusters with Fuzzy C-means clustring.
distfcm.m	Distances measure in Fuzzy C-means clustering.
initfcm.m	Generate initial fuzzy partition martix for FCM .
stepfcm.m	One step in FCM.
HCM
hcmtest.m	Find centers of Hard C-means clustering with 2-D datasets.
hcm.m	Find clusters with Hard C-means clustring.
inithcm.m	Generate initial cluster center s by randomly selecting C points fromamong all of the data points.
disthcm.m	Distances measure between all points ans centers with HCM.
mfuhcm.m	Determine the menbership matrix U of hard C-means.
比較
compare.m	This program is used to compare the performance between hard C-meansand fuzzy C-means.
sepclust.m	SEPCLUST seprate clusters by finding the minimum distance between clustercentroids.
pihcm.m	PIHCM finds the total variation to the size of the data set in HardC-means.
pifcm.m	PIFCM finds the total variation to the size of the data set in FuzzyC-means.
combinate.m	out = combinat(obj, n) returns combinations of obj with n distinctelements. For instance: combinat([1 2 3 4 5], 2) or combinat('abcde', 3).