Hard Clustering - MLforSEO

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Hard Clustering

A type of clustering where data points are assigned exclusively to a single cluster.

Hard clustering is a clustering approach where each customer or data point is assigned exclusively to a single segment or cluster. The resulting segments are non-overlapping; a data point belongs either to one cluster or the other, based on specific criteria like purchase history or demographics.
This method is best employed when customer segments are definitively distinct and marketing strategies need to target non-overlapping groups, such as running specific loyalty programs. K-Means and DBSCAN are examples of hard clustering algorithms.

Sources & References

Introduction to Machine Learning for SEOs Course

academy.mlforseo.com

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