Mean-Shift Clustering Algorithm

Introduction

This lab will guide you through the process of implementing the Mean-Shift Clustering Algorithm using the Scikit-learn library in Python. You will learn how to generate sample data, compute clustering with MeanShift, and plot the results.

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Skills Graph

Import Required Libraries

First, we need to import the required libraries: numpy, sklearn.cluster, sklearn.datasets, and matplotlib.pyplot.

import numpy as np
from sklearn.cluster import MeanShift, estimate_bandwidth
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt

Generate Sample Data

Next, we will generate sample data using the make_blobs function from the sklearn.datasets module. We will create a dataset with 10,000 samples and three clusters with centers at [[1, 1], [-1, -1], [1, -1]] and a standard deviation of 0.6.

centers = [[1, 1], [-1, -1], [1, -1]]
X, _ = make_blobs(n_samples=10000, centers=centers, cluster_std=0.6)

Compute Clustering with MeanShift

Now we will compute clustering using the Mean-Shift algorithm with the MeanShift class from the sklearn.cluster module. We will use the estimate_bandwidth function to automatically detect the bandwidth parameter, which represents the size of the region of influence for each point.

bandwidth = estimate_bandwidth(X, quantile=0.2, n_samples=500)
ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)
ms.fit(X)
labels = ms.labels_
cluster_centers = ms.cluster_centers_

Plot Results

Finally, we will plot the results using the matplotlib.pyplot library. We will use different colors and markers for each cluster, and we will also plot the cluster centers.

plt.figure(1)
plt.clf()

colors = ["#dede00", "#377eb8", "#f781bf"]
markers = ["x", "o", "^"]

for k, col in zip(range(n_clusters_), colors):
    my_members = labels == k
    cluster_center = cluster_centers[k]
    plt.plot(X[my_members, 0], X[my_members, 1], markers[k], color=col)
    plt.plot(
        cluster_center[0],
        cluster_center[1],
        markers[k],
        markerfacecolor=col,
        markeredgecolor="k",
        markersize=14,
    )
plt.title("Estimated number of clusters: %d" % n_clusters_)
plt.show()

Summary

In this lab, we learned how to implement the Mean-Shift Clustering Algorithm using the Scikit-learn library in Python. We generated sample data, computed clustering with MeanShift, and plotted the results. By the end of this lab, you should have a better understanding of how to use the Mean-Shift algorithm to cluster data.