Clustering in data mining is a technique used to group a set of data points into clusters, where data points within the same cluster are more similar to each other than to those in other clusters. It’s an unsupervised learning method, meaning it doesn’t rely on predefined labels but instead identifies patterns based on the inherent structure of the data.

Key Points:

• Objective: The goal of clustering is to organize data into meaningful groups based on their similarities, making it easier to analyze and draw insights.
• Applications:
  • Customer Segmentation: Grouping customers with similar purchasing behaviors.
  • Image Compression: Grouping pixels with similar colors.
  • Anomaly Detection: Identifying outliers that don't fit any cluster.

Common Clustering Algorithms:

1. K-means: Divides data into K clusters based on their centroids.
2. Hierarchical Clustering: Builds a tree of clusters based on data similarity.
3. DBSCAN: Groups together points that are closely packed and marks outliers.

Example:

In a retail store, clustering can group customers into segments like "frequent buyers," "discount shoppers," and "occasional buyers" based on their purchasing behavior. This helps in targeting specific marketing strategies for each group.