K-Means Clusters Overview

K-Means Clusters Overview

K-Means is an unsupervised machine learning algorithm used for clustering data into distinct groups based on similarity. It is widely used in pattern recognition, market segmentation, and anomaly detection.

How K-Means Works

  1. Initialize Clusters: Select kkk cluster centroids randomly.
  2. Assign Data Points: Each data point is assigned to the nearest cluster centroid.
  3. Update Centroids: The centroids are recalculated as the mean of all data points in each cluster.
  4. Repeat: Steps 2 and 3 are repeated until centroids stop changing or a predefined number of iterations is reached.

Key Components

  • Centroids: The center points of the clusters.
  • Distance Measure: Euclidean distance is commonly used to measure similarity.
  • Number of Clusters (k): Needs to be predefined, often chosen using the Elbow Method or Silhouette Score.   

Advantages

  1.  Simple and easy to implement.
  2.  Works well on large datasets.
  3.  Scalable and efficient.

Disadvantages

  1.  Requires choosing kkk manually.
  2. Sensitive to outliers and initial centroid placement.
  3.  Assumes clusters are spherical and equal in size.

Reference: Some of the text in this article has been generated using AI tools such as ChatGPT and edited for content and accuracy.

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