Clustering is the process of grouping a set of data points into clusters based on their similarities.
Clustering is used in various applications such as customer segmentation, image segmentation, and anomaly detection. It helps in identifying natural groupings within the data for better analysis and decision-making.
The process involves using algorithms to find patterns and groupings in the data without predefined labels. The model learns to identify clusters based on the inherent structure of the data.
Clustering can be either hard (each data point belongs to exactly one cluster) or soft (each data point can belong to multiple clusters with different probabilities).
Common algorithms used for clustering include K-Means, Hierarchical Clustering, and DBSCAN.
Understanding and implementing clustering techniques is essential for uncovering hidden patterns in data and making informed decisions based on these patterns.
- Related terms
- Unsupervised Learning K-Means Hierarchical Clustering Classification Regression