Clustering is a method of unsupervised learning used in machine learning. It 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 such as data mining, pattern recognition, and image processing.

Types of Clustering Techniques

There are several types of clustering techniques, each with its own characteristics and use cases:

  • Hierarchical Clustering: This method builds a hierarchy of clusters. It can be either agglomerative (bottom-up) or divisive (top-down).
  • K-Means Clustering: This is one of the most popular clustering algorithms. It divides the dataset into K clusters, where K is specified by the user.
  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise): This algorithm groups together points that are closely packed together, marking as outliers the points that lie alone in low-density regions.
  • Gaussian Mixture Models (GMM): This technique assumes that the data is generated from a mixture of Gaussian distributions.

Clustering Algorithms and Use Cases

K-Means Clustering

K-Means clustering is useful for finding groups in large datasets. It is often used for customer segmentation, image segmentation, and anomaly detection.

DBSCAN

DBSCAN is particularly useful for finding clusters of arbitrary shapes. It is often used in bioinformatics and image processing.

Gaussian Mixture Models

GMM is useful for modeling data that is believed to come from multiple Gaussian distributions. It is often used in natural language processing and image processing.

Further Reading

For more information on clustering techniques, you can refer to the following resources:

K-Means Clustering