Explaining k-means clustering and it’s Real use cases in the Security Domain

Arth Task-42

What does K mean in k-means clustering?

K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. To achieve this objective, K-means looks for a fixed number (k) of clusters in a dataset.” A cluster refers to a collection of data points aggregated together because of certain similarities.

What is k-means clustering used for?

The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data. This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets.

What are the steps in 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: Recomputed the centroids of newly formed clusters. …
• Step 5: Repeat steps 3 and 4.

Use-case of K-means Clustering in Security Domain

The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data. This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets.

k-means can typically be applied to data that has a smaller number of dimensions, is numeric, and is continuous. think of a scenario in which you want to make groups of similar things from a randomly distributed collection of things k-means is very suitable for such scenarios.

Examples:

Behavioral segmentation, Segment by purchase history, Segment by activities on application, website, or platform, Define personas based on interests, Create profiles based on activity monitoring, Inventory categorization, Group inventory by sales activity, Group inventory by manufacturing metrics, Sorting sensor measurements, Detect activity types in motion sensors, Group images