Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

Chapter 4
Unsupervised Learning: Clustering
if source[-1] == target[-1]:
cost = 0
else:
cost = 1
res = min([ED(source[:-1], target)+1,
ED(source, target[:-1])+1,
ED(source[:-1], target[:-1]) + cost])
return res
def find_centre(x, X, mu):
min = 100
cent = 0
for c in mu:
dist = ED(x, X[c])
if dist< min:
min = dist
cent = c
return cent
def cluster_arrange(X, cent):
clusters = {}
for x in X:
bestcent = find_centre(x, X, cent)
try:
clusters[bestcent].append(x)
exceptKeyError:
clusters[bestcent] = [x]
return clusters
def rearrange_centers(cent, clusters):
newcent = []
keys = sorted(clusters.keys())
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