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What are the main steps involved in the K-means clustering algorithm?

Revati S Misra20828-May-2023

What are the main steps involved in the K-means clustering algorithm?

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Updated on 29-May-2023
Revati S Misra

Revati S Misra

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Aryan Kumar

Aryan Kumar

29-May-2023

The K-means clustering algorithm is an iterative algorithm that partitions a dataset into K clusters. Here are the main steps involved in the K-means clustering algorithm:

Initialization:

  • Choose the number of clusters K.
  • Randomly initialize K cluster centroids (points that represent the center of each cluster).

Assignment Step:

  • Assign each data point to the nearest cluster centroid based on the Euclidean distance or other distance metrics.
  • Calculate the distance between each data point and each centroid and assign the data point to the cluster with the closest centroid.

Update Step:

  • Recalculate the centroids of the K clusters based on the current assignments.
  • Calculate the mean or centroid of each cluster by averaging the coordinates of all data points assigned to that cluster.

Iteration:

  • Repeat the Assignment Step and Update Step until convergence or until a specified number of iterations is reached.
  • In each iteration, the data points are reassigned to the nearest cluster centroid, and the centroids are updated.

Convergence:

  • The algorithm converges when the assignments and centroids no longer change significantly between iterations or when the maximum number of iterations is reached.

Output:

  • The final output of the K-means algorithm is the K cluster centroids and the assignment of data points to clusters.

Optional: Evaluation and Interpretation:

  • Evaluate the quality of the clustering results using metrics such as the within-cluster sum of squares (WCSS) or silhouette score.
  • Interpret and analyze the characteristics of each cluster based on the assigned data points and centroid features.

It's important to note that K-means clustering is sensitive to the initial random centroids' selection, and the algorithm may converge to a suboptimal solution. Therefore, it is common to run the algorithm multiple times with different initializations to increase the chances of finding a better clustering result.

The K-means clustering algorithm is widely used in various applications, including customer segmentation, image segmentation, document clustering, and pattern recognition, where the goal is to partition data into distinct groups based on their similarities.

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