Clustering

Clustering is a data mining technique used to group similar data points together. The goal of clustering is to identify patterns and groupings in datasets that may not be immediately apparent. Clustering is an unsupervised machine learning technique, which means it doesn't rely on labeled data to produce its results.

Types of Clustering

There are two main types of clustering:

• Hierarchical clustering involves grouping data points together based on their similarity.
• K-means clustering involves partitioning data into k groups based on their distance from the center of each group.

An example of clustering in action is customer segmentation in marketing. A company may use clustering to group customers based on their purchasing behavior, demographic information, or other factors. This information can then be used to target specific groups of customers with personalized marketing campaigns.

Clustering algorithms can be computationally intensive and require careful consideration of the number of clusters to use. However, they can be powerful tools for uncovering hidden patterns in large datasets.

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