K-means Clustering and its real use-case in the Security Domain

K-means Algorithm

  1. Specify number of clusters K.
  2. Initialize centroids by first shuffling the dataset and then randomly selecting K data points for the centroids without replacement.
  3. Keep iterating until there is no change to the centroids. i.e assignment of data points to clusters isn’t changing.
  • Compute the sum of the squared distance between data points and all centroids.
  • Assign each data point to the closest cluster (centroid).
  • Compute the centroids for the clusters by taking the average of the all data points that belong to each cluster.

Use-Cases in the Security Domain

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Aditi Awasthi

Aditi Awasthi

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