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The task of grouping data points into groups (clusters) such that points in a group are more ‘similar’ to each other than to points outside the group is called clustering. But how does one know if a data point is similar to another point or not? This act of defining similarity is what distinguishes various clustering methods from each other — K-Means defines similarity by the closeness of a data point to the centroid of the clusters while DBSCAN defines similarity by grouping together data points that are within the same density region.

In this article, we’ll take a look at these two clustering methods that are often used in unsupervised machine learning and implement them in Python. …


James Issac

A whole new world. That’s where we’ll be.

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