Clustering
An unsupervised learning technique that groups similar data points together based on their characteristics, without predefined labels. The algorithm discovers natural groupings in the data.
Why It Matters
Clustering reveals hidden patterns and segments in data, enabling targeted marketing, anomaly detection, and data exploration without needing labeled examples.
Example
An e-commerce company automatically grouping customers into segments like 'bargain hunters,' 'luxury shoppers,' and 'occasional browsers' based on behavior data.
Think of it like...
Like sorting a jar of mixed buttons by color, size, and shape without being told the categories — you naturally create groups of similar buttons.
Related Terms
K-Means
A clustering algorithm that partitions data into K groups by iteratively assigning each data point to the nearest cluster center and then recalculating the centers. K must be specified in advance.
Unsupervised Learning
A type of machine learning where the model learns patterns from unlabeled data without being told what the correct output should be. The algorithm discovers hidden structures, groupings, or patterns in the data on its own.
Dimensionality Reduction
Techniques that reduce the number of features (dimensions) in a dataset while preserving the most important information. This makes data easier to visualize, speeds up training, and can improve model performance.