Python Program to Implement Clustering Assignment Solution.

import pandas as pd

import numpy as np

from matplotlib import pyplot as plt

from scipy.cluster.hierarchy import dendrogram, linkage

"""# Read original data with all coefficients

We will read the original .csv file and then extract the desired column

"""

data = pd.read_csv('original_data.csv')

# drop na

data = data.dropna()

data.head()

"""# Get data for column 'Degree = 4 Coefficients'

The column contains the points in a string '[ .. ]', so we will have to parse that string to remove the brackets and extract the float values

"""

pointsraw = data['Degree=4 Coefficients'].to_numpy() # extract values and convert to numpy

# Now, take each row, remove first and last characters ( [] ), and split

X = np.zeros((pointsraw.shape[0], 5)) # Matrix to store all 39 samples

# Loop through each raw sample

for i, points_str in enumerate(pointsraw):

points_str = points_str[1:-1] # remove first and last characters which are []

# Split

points_lst = points_str.split()

# Convert to float

points_i = list(map(float, points_lst))

# Add to matrix

X[i,:] = points_i

"""# Hierarchical Clustering"""

Z = linkage(X, method = 'ward', metric = 'euclidean')

"""# Dendogram"""

# Create figure

plt.figure(figsize=(25, 15))

# Create dendogram

dendrogram(

Z,

leaf_rotation=90., # rotates the x axis labels

leaf_font_size=8., # font size for the x axis labels

)

plt.title('Hierarchical Clustering Dendrogram', fontsize=25)

plt.xlabel('Index', fontsize=25)

plt.ylabel('Euclidean Distance', fontsize=25)

plt.show()