Write a program  for Comparative Analysis of K-means and Minimum Spanning Tree (MST) Based Clustering Techniques

Sure, I can provide a basic outline for a Python program that compares K-means and MST-based clustering techniques. However, please note that this is a complex task and requires a good understanding of both techniques and the Python language.

Here is a basic outline:

# Import necessary libraries
from sklearn.cluster import KMeans
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import minimum_spanning_tree
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
import numpy as np

# Generate a random dataset for clustering
X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)

# K-means clustering
kmeans = KMeans(n_clusters=4)
kmeans.fit(X)
y_kmeans = kmeans.predict(X)

# Plotting K-means clustering result
plt.scatter(X[:, 0], X[:, 1], c=y_kmeans, s=50, cmap='viridis')
plt.title('K-means Clustering')
plt.show()

# MST-based clustering
# First, we need to create a distance matrix
dist_matrix = np.zeros((300,300))
for i in range(300):
    for j in range(300):
        dist_matrix[i,j] = np.sqrt(np.sum((X[i]-X[j])**2))

# Create a sparse matrix
X_csr = csr_matrix(dist_matrix)

# Compute the minimum spanning tree
Tcsr = minimum_spanning_tree(X_csr)

# Plotting MST-based clustering result
plt.spy(Tcsr)
plt.title('MST-based Clustering')
plt.show()

This program first generates a random dataset for clustering. Then it applies K-means clustering and plots the result. After that, it creates a distance matrix for the MST-based clustering. It computes the minimum spanning tree and plots the result.

Please note that this is a very basic comparison. A more thorough comparison would involve multiple datasets, different parameters, and more sophisticated evaluation metrics.