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

Unsupervised Learning

Clustering

Types of Clustering

  • Hierarchical clustering
  • Partitioning clustering
  • Agglomerative clustering
  • Divisive clustering
  • K-Means clustering
  • Fuzzy C-Means clustering

K-means Clustering

Where does the k-means clustering algorithm is used?

What are the basic steps for K-means clustering?

  • Step 1: Choose the number of clusters k.
  • Step 2: Select k random points from the data as centroids.
  • Step 3: Assign all the points to the closest cluster centroid.
  • Step 4: Re-compute the centroids of newly formed clusters.
  • Step 5: Repeat steps 3 and 4.

Applications of K-Means Clustering

  • Academic performance
  • Diagnostic systems
  • Search engines
  • Wireless sensor networks

How Does K-Means Clustering Work?

Limitations of K-means Clustering

  1. The output is highly influenced by original input, for example, the number of clusters.
  2. An array of data substantially hits the concluding outcomes.
  3. In some cases, clusters show complex spatial views, then executing clustering is not a good choice.
  4. Also, rescaling is sometimes conscious, it can’t be done by normalization or standardization of data points, the output gets changed entirely.

K-Means Use-Cases in the Security Domain

  1. Identifying crime localities-

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DevOps, MLOps, Machine Learning,Cloud, Flutter, J2EE, python, Web Development

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Ajay Mory

Ajay Mory

DevOps, MLOps, Machine Learning,Cloud, Flutter, J2EE, python, Web Development

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