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

Unsupervised Learning

Clustering

Types of Clustering

Clustering is a type of unsupervised learning wherein data points are grouped into different sets based on their degree of similarity.

  • 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?

The k-mean clustering algorithm is used in Machine Learning models where we have to do unsupervised learning with improper historical data, so for that case, we use the k-means clustering algorithm.

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

K-Means clustering is used in a variety of examples or business cases in real life, like:

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

How Does K-Means Clustering Work?

The flowchart below shows how k-means clustering works:

Limitations of K-means Clustering

Sometimes, it is quite tough to forecast the number of clusters, or the value of k.

  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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Ajay Mory

Ajay Mory

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