kmeans python

#kmeans
import math
def doKmeansCluster(data, cnum, itnum):
c = data[:cnum]
for time in range(itnum):
groups = [[] for i in range(len(c))]
for d in data:
min = distance(d,c[0])
index = 0
for i in range(len(c)):
dis = distance(d,c[i])
index = [index,i][dis<min]
min = [min,dis][dis<min]
groups[index].append(d)

c = []
for g in groups:
print g
#transport the matrix, make all measure of the same demision in one same list
trans = [[r[col] for r in g] for col in range(len(g[0]))]
#get new center by sum and divide
avg = [float(sum(trans[i]))/float(len(trans[0])) for i in range(len(trans))]
c.append(avg)

def distance(a, b):
return math.sqrt(sum([math.pow(a[i]-b[i],2) for i in range(len(a))]))