09 || Simple Clustering Algorithms for Unsupervised Learning

Clustering is an important task in unsupervised learning, where the goal is to group similar data points together based on some criteria. There are many different clustering algorithms available, each with its own strengths and weaknesses. In this tutorial, we will focus on three simple clustering algorithms: K-means, Hierarchical clustering, and DBSCAN.

1. K-means clustering:
   K-means is one of the most widely used clustering algorithms due to its simplicity and efficiency. The algorithm works by iteratively assigning each data point to the nearest cluster centroid and then recalculating the centroid based on the mean of the data points assigned to it.

Here’s how the K-means algorithm works:

1. Initialize k cluster centroids randomly.
2. Assign each data point to the nearest cluster centroid.
3. Recalculate the centroid of each cluster based on the mean of the data points assigned to it.
4. Repeat steps 2 and 3 until convergence (i.e., when the cluster assignments no longer change).

To implement K-means clustering in Python, you can use the sklearn library:

from sklearn.cluster import KMeans
import numpy as np

# Generate some random data
X = np.random.rand(100, 2)

# Create a KMeans object with 3 clusters
kmeans = KMeans(n_clusters=3)

# Fit the model to the data
kmeans.fit(X)

# Get the cluster labels
labels = kmeans.labels_

# Get the cluster centroids
centroids = kmeans.cluster_centers_

1. Hierarchical clustering:
   Hierarchical clustering is a bottom-up approach where each data point starts as its own cluster, and clusters are successively merged based on their similarity. There are two main types of hierarchical clustering: agglomerative and divisive.

Agglomerative hierarchical clustering works as follows:

1. Start with each data point as its own cluster.
2. Merge the two closest clusters into a new cluster.
3. Repeat step 2 until all data points are in one cluster.

To implement agglomerative hierarchical clustering in Python, you can use the scipy library:

from scipy.cluster.hierarchy import linkage, dendrogram
import matplotlib.pyplot as plt
import numpy as np

# Generate some random data
X = np.random.rand(100, 2)

# Perform hierarchical clustering
Z = linkage(X, method='single')

# Plot the dendrogram
plt.figure(figsize=(10, 5))
dendrogram(Z)
plt.show()

1. DBSCAN clustering:
   DBSCAN is a density-based clustering algorithm that can identify clusters of arbitrary shapes and sizes. The algorithm works by finding core samples (data points with a minimum number of neighbors) and expanding clusters around them.

Here’s how the DBSCAN algorithm works:

1. Start with an arbitrary data point.
2. Find all data points within a specified radius (eps) of the starting point.
3. If the number of points within eps is greater than a specified minimum points (min_samples), start a new cluster with the starting point as the core sample.
4. Expand the cluster by adding neighboring points to it.
5. Repeat steps 2-4 for all data points not assigned to a cluster.

To implement DBSCAN clustering in Python, you can use the sklearn library:

from sklearn.cluster import DBSCAN
import numpy as np

# Generate some random data
X = np.random.rand(100, 2)

# Create a DBSCAN object
dbscan = DBSCAN(eps=0.1, min_samples=5)

# Fit the model to the data
dbscan.fit(X)

# Get the cluster labels
labels = dbscan.labels_

In this tutorial, we have covered three simple clustering algorithms: K-means, hierarchical clustering, and DBSCAN. Each algorithm has its own strengths and weaknesses, so it’s important to choose the right one based on your data and the problem at hand. Experiment with different algorithms and parameters to find the best clustering solution for your data.