Python Program to Implement Clustering Assignment Solution

Instructions

Objective

Write a program to implement clustering in python.

Requirements and Specifications

Source Code

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()