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Discretization using clustering is a technique that transforms continuous data into discrete categories by grouping similar data points together. This is accomplished by applying clustering algorithms, such as k-means or hierarchical clustering, to identify natural groupings in the data. Each cluster represents a discrete interval or category, allowing for easier analysis and interpretation of the data. This method is particularly useful in machine learning and data mining applications where categorical input is required.

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