Machine learning clustering is a method of grouping data points into clusters, which are subsets of the data that are similar in some way. This technique is widely used in various fields such as data analysis, pattern recognition, and image processing.
Clustering Techniques
There are several clustering techniques available:
K-Means Clustering: This is one of the most commonly used clustering algorithms. It aims to partition the dataset into K distinct, non-overlapping subgroups (clusters) where each data point belongs to the cluster with the nearest mean (center).
Hierarchical Clustering: This method creates a hierarchy of clusters. It can be either agglomerative (bottom-up) or divisive (top-down).
DBSCAN (Density-Based Spatial Clustering of Applications with Noise): DBSCAN is a density-based clustering algorithm that can discover clusters of arbitrary shape.
Applications of Clustering
Clustering has a wide range of applications, including:
Market Segmentation: In marketing, clustering is used to group customers into segments based on their buying behavior, demographics, or other characteristics.
Image Segmentation: Clustering is used to segment images into different regions, which can be useful for object recognition and image processing tasks.
Document Clustering: Clustering is used to organize documents into groups based on their content.
For more information on machine learning clustering, you can read our comprehensive guide on Machine Learning Basics.