<reponame>MahmoudAbusaqer/Clustering.py
import pandas as pd

iris = pd.read_csv('IRIS.csv')
# print(iris.head(5))

# split data attributes and label attribute
attributes = iris.drop(['species'], axis=1)
labels = iris['species']

# import and build hieratchy cluster model
from scipy.cluster.hierarchy import linkage, dendrogram
hc = linkage(attributes, 'single')
# print(hc)

# plot the dendogram
samplelist = range(1, 151)# make a list for the data samples

# import pylot libray
from matplotlib import pyplot as plt

plt.figure(figsize=(30, 15))
dendrogram(hc,
           orientation='top',
           labels=samplelist,
           distance_sort='descending',
           show_leaf_counts='true')

plt.show()
import pandas as pd

iris = pd.read_csv('IRIS.csv')
# print(iris.head(5))

# split data attributes and label attribute
attributes = iris.drop(['species'], axis=1)
labels = iris['species']

# import and build hieratchy cluster model
from scipy.cluster.hierarchy import linkage, dendrogram
hc = linkage(attributes, 'single')
# print(hc)

# plot the dendogram
samplelist = range(1, 151)# make a list for the data samples

# import pylot libray
from matplotlib import pyplot as plt

plt.figure(figsize=(30, 15))
dendrogram(hc,
           orientation='top',
           labels=samplelist,
           distance_sort='descending',
           show_leaf_counts='true')

plt.show()
# print(iris.head(5)) # split data attributes and label attribute # import and build hieratchy cluster model # print(hc) # plot the dendogram # make a list for the data samples # import pylot libray