Clustering is a method of unsupervised 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 technique is widely used in various fields, including data mining, pattern recognition, and image processing.

Key Concepts

  • Similarity: The measure of how similar two objects are.
  • Distance: The measure of the difference between two objects.
  • Cluster: A group of objects that are similar to each other.

Types of Clustering

  1. Hierarchical Clustering: This method creates a hierarchy of clusters.
  2. Partitioning Clustering: This method divides the data into distinct, non-overlapping subsets.
  3. Density-Based Clustering: This method groups together objects that are dense in space relative to regions of lower density.
  4. Model-Based Clustering: This method assumes that the data follows a specific distribution and tries to find the best fit for that distribution.

Applications

  • Market Segmentation: Grouping customers based on their purchasing behavior.
  • Image Segmentation: Grouping similar pixels together in an image.
  • Document Clustering: Grouping similar documents together.

Clustering Diagram

For more information on clustering, you can read our detailed guide on Clustering Techniques.