K-mean clustering

How does the K-Means Algorithm Work?

The working of the K-Means algorithm is explained in the below steps:

K-mean Clustering Use-case in Security:

Advantages of K-means

  1. It is very simple to implement.
  2. It is scalable to a huge data set and also faster to large datasets.
  3. it adapts the new examples very frequently.
  4. Generalization of clusters for different shapes and sizes.

Disadvantages of K-means

  1. It is sensitive to the outliers.
  2. Choosing the k values manually is a tough job.
  3. As the number of dimensions increases its scalability decreases.

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