Welcome to the clustering tutorial! Clustering is a technique in machine learning that involves grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. This tutorial will guide you through the basics of clustering and its applications.

What is Clustering?

Clustering is a method of unsupervised learning. Unlike supervised learning, where the data is labeled, in clustering, the data is not labeled. The goal of clustering is to discover the underlying structure in the data.

Types of Clustering

  • Hierarchical Clustering
  • K-Means Clustering
  • DBSCAN Clustering

K-Means Clustering

K-Means is one of the most popular clustering algorithms. It aims to partition the dataset into k distinct, non-overlapping subgroups (clusters) where each data point belongs to only one group.

Steps of K-Means

  1. Initialize k centroids randomly.
  2. Assign each data point to the nearest centroid.
  3. Recompute the centroids as the mean of the assigned points.
  4. Repeat steps 2 and 3 until convergence.

DBSCAN Clustering

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

Key Concepts

  • Core Points: Points that have at least min_samples neighboring points.
  • Border Points: Points that are not core points but are within the ε neighborhood of a core point.
  • Noise Points: Points that are not core or border points.

Applications of Clustering

  • Market Segmentation
  • Customer Behavior Analysis
  • Image Segmentation

Further Reading

For more in-depth knowledge on clustering, check out our comprehensive guide on Clustering Techniques.

K-Means Clustering

DBSCAN Clustering