Clustering Algorithms Explained

Clustering algorithms are a fundamental concept in machine learning. They help us to identify patterns and group similar data points together. In this tutorial, we will explore the basics of clustering algorithms and their applications.

Types of Clustering Algorithms

There are several types of clustering algorithms, each with its own strengths and weaknesses. Here are some of the most common ones:

K-Means Clustering
Hierarchical Clustering
DBSCAN
Gaussian Mixture Models (GMM)

K-Means Clustering

K-Means clustering is one of the simplest and most commonly used clustering algorithms. It divides the data into K clusters, where K is a predefined number. The algorithm tries to minimize the distance between the points in each cluster.

Hierarchical Clustering

Hierarchical clustering creates a tree-like structure of clusters. It starts with each data point as a separate cluster and then merges the closest clusters together until all points are in one cluster.

DBSCAN

DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm. It groups together points that are closely packed together and marks as outliers points that lie alone in low-density regions.

Gaussian Mixture Models (GMM)

Gaussian Mixture Models (GMM) assume that the data is generated from a mixture of Gaussian distributions. Each cluster is represented by a Gaussian distribution.

Applications of Clustering Algorithms

Clustering algorithms have a wide range of applications, including:

Market Segmentation
Anomaly Detection
Image Segmentation
Document Clustering