Every day, companies collect huge amounts of data—but raw data by itself isn’t helpful unless you can find patterns inside it. That’s where cluster analysis in data mining becomes useful.
Cluster analysis is a method of grouping data points so that items in the same group (or cluster) are more similar to each other than to those in other groups. Think of it like arranging your music playlist—grouping songs by mood or genre without anyone labelling them for you.
For example, an e-commerce platform can use clustering to group customers based on their shopping habits—like “budget shoppers,” “frequent buyers,” or “premium users.” This helps in personalising recommendations and offers.
Types of Cluster Analysis You Should Know
There are different ways to perform clustering:
Hierarchical Clustering – builds clusters step by step, either bottom-up or top-down
Partitioning Methods – like K-Means, where you predefine the number of clusters
Density-Based Clustering – groups data based on high-density regions (DBSCAN is common)
Model-Based Clustering – assumes a statistical model for each cluster
Real-World Applications
Cluster analysis in data mining is used in many areas—customer segmentation in retail, patient grouping in healthcare, risk profiling in banking, and audience targeting in marketing.
Want to try it yourself? At Ze Learning Labb, our Data Science and Data Analytics courses teach you how to apply clustering using tools like Python, R, and SQL on real datasets.
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