VISTA ACADEMY · MACHINE LEARNING

Lesson 19: Clustering Algorithms

Explore unlabeled data with K-Means, DBSCAN, hierarchical clustering, and practical validation.

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Introduction to Clustering

Clustering is an unsupervised learning approach that groups observations based on similarity. Unlike classification, it does not start with predefined target labels. The groups it discovers still need interpretation and validation.

Find patterns without labels

A retailer has customer purchase histories but no customer-segment labels. Clustering can group similar purchasing patterns, helping analysts explore potential segments before deciding what they mean.

BUSINESS ACTIVITY

A marketing team wants to explore groups of customers using purchase behavior, but has no existing segment labels. Which technique fits?

ClusteringSupervised classification onlyDate formatting

CONCEPT CHECK

What best describes clustering?

Grouping observations by similarity without predefined labelsPredicting a known target label for every rowSorting records alphabeticallyReplacing all missing values with zero

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Why this matters at work

Clustering helps explore customer behavior, group similar records, and discover structure in unlabeled data. Reliable use requires checking assumptions, interpreting the groups, and validating actions.

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