What is Clustering?

Clustering helps to discover the inherent structure in data without prior knowledge of the categories. It is widely used in various applications, from customer segmentation to image analysis.

Key Concepts in Clustering

• Centroids: The center of a cluster, often used in algorithms like k-means.
• Distance Metrics: Measures such as Euclidean distance used to determine the similarity between data points.
• Hierarchical Clustering: A method that builds nested clusters by successively merging or splitting clusters.

Applications of Clustering

• Market Segmentation: Groups customers based on purchasing behavior for targeted marketing.
• Image Segmentation: Used in computer vision to partition images into meaningful segments.
• Anomaly Detection: Identifies outliers that do not fit into any cluster.

Conclusion

Clustering is a fundamental technique in unsupervised learning, providing insights into the natural grouping of data and enabling more targeted and effective decision-making.