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

Chapter 4
Unsupervised Learning: Clustering
To create a cluster from a dendrogram, you need a threshold of
distance or similarity. An easy way to do this is to plot the distribution
of distance or similarity and find the inflection point of the curve. For
Gaussian distribution data, the inflection point is located at x = mean +
n*std and x = mean – n*std, as shown Figure 4-3.
Figure 4-3. The inflection point
The following code creates a hierarchical cluster using Python:
From numpy import *
class cluster_node:
def \ __init__(self,vec1,left1=None,right1=None,distance1=0.0,i
d1=None,count1=1):
self.left1=left1
self.right1=right1
self.vec1=vec1
self.id1=id1
self.distance1=distance1
self.count1=count1 #only used for weighted average
def L2dist(v1,v2):
return sqrt(sum((v1-v2)**2))
def hcluster(features1,distanc1e=L2dist):
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