Python Code for Fuzzy C-Means Clustering

Python Code for Fuzzy C-Means Clustering

	# Importing libraries
	from __future__ import division, print_function
	import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt
	import skfuzzy as fuzz
	colors = ['b', 'orange', 'g', 'r', 'Brown', 'ForestGreen', 'c', 'm', 'y', 'k']
	# Define three cluster centers
	centers = [[6, 3],
	[2, 8],
	[4, 5]]
	# Define three cluster sigmas in x and y, respectively
	sigmas = [[0.7, 0.4],
	[0.4, 0.6],
	[1.0, 0.6]]
	# Importing dataset
	data = pd.read_csv("data.csv")
	xpts = data.iloc[:,0]
	ypts = data.iloc[:,1]
	labels = data.iloc[:,2]
	# Visualize the data
	fig0, ax0 = plt.subplots()
	for label in range(3):
	ax0.plot(xpts[labels == label], ypts[labels == label], '.',
	color=colors[label])
	ax0.set_title('Test data: 200 points x3 clusters.')
	# Set up the loop and plot
	fig1, axes1 = plt.subplots(3, 3, figsize=(8, 8))
	alldata = np.vstack((xpts, ypts))
	fpcs = []
	for ncenters, ax in enumerate(axes1.reshape(-1), 2):
	cntr, u, u0, d, jm, p, fpc = fuzz.cluster.cmeans(
	alldata, ncenters, 2, error=0.005, maxiter=1000, init=None)
	# Store fpc values for later
	fpcs.append(fpc)
	# Plot assigned clusters, for each data point in training set
	cluster_membership = np.argmax(u, axis=0)
	for j in range(ncenters):
	ax.plot(xpts[cluster_membership == j],
	ypts[cluster_membership == j], '.', color=colors[j])
	# Mark the center of each fuzzy cluster
	for pt in cntr:
	ax.plot(pt[0], pt[1], 'rs')
	ax.set_title('Centers = {0}; FPC = {1:.2f}'.format(ncenters, fpc))
	ax.axis('off')
	fig1.tight_layout()

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Output:

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