K-Means clustering and its use cases

What is meant by K-means clustering?

K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K.

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: Recompute the centroids of newly formed clusters.
• Step 5: Repeat steps 3 and 4.

How does K mean clustering works explain with example?

K-means clustering uses “centroids”, K different randomly-initiated points in the data, and assigns every data point to the nearest centroid. After every point has been assigned, the centroid is moved to the average of all of the points assigned to it.

USE-CASES of K mean clustering in security domain

fraud detection

Machine Learning has a critical role to play in fraud detection and has numerous applications in automobile, healthcare, and insurance fraud detection. Utilizing past historical data on fraudulent claims, it is possible to isolate new claims based on its proximity to clusters that indicate fraudulent patterns. Since insurance fraud can potentially have a multi-million dollar impact on a company, the ability to detect frauds is crucial.

Cyber-profiling criminals

Cyber-profiling is the process of collecting data from individuals and groups to identify significant co-relations. The idea of cyber profiling is derived from criminal profiles, which provide information on the investigation division to classify the types of criminals who were at the crime scene.

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