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Learn AI with Python · Lesson

K-Means Clustering

Step-by-step understanding.

1

K-Means Clustering

K-Means is one of the most popular clustering algorithms. It partitions data into a predefined number of clusters (K) by minimizing the intra-cluster distances.

It works iteratively to find the best cluster assignments and centroids.

K-Means Clustering — illustration 1

2

How K-Means Works

K-Means operates in the following steps:

  1. Initialize K random centroids.
  2. Assign each data point to the nearest centroid.
  3. Recalculate centroids as the mean of all points in a cluster.
  4. Repeat steps 2 and 3 until centroids stabilize or a maximum number of iterations is reached.

All lessons in this course

  1. Introduction to Clustering Algorithms
  2. K-Means Clustering
  3. K-Means Clustering Project
  4. Dimensionality Reduction Basics
  5. Dimensionality Reduction Application
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