K-MEANS clustering and its real use case in the security domain.
What is Clustering?
Clustering as a term means grouping identical elements into similar groups or clusters. It can be defined as a process of breaking down the data into groups consisting of similar data points or elements.
What is unsupervised learning?
Unsupervised learning uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without the need for human intervention. Its ability to discover similarities and differences in information make it the ideal solution for exploratory data analysis, cross-selling strategies, customer segmentation, and image recognition.
What is K-Means Clustering?
K-means clustering is one of the simplest and popular unsupervised machine learning algorithms.
K-Means algorithm uses the clustering method to group identical data points in one group and all the data points in that group share common features but are distinct when compared to data points in other groups.
How does the K-Means Algorithm Work?
The working of the K-Means algorithm is explained in the below steps:
Step-1: Select the number K to decide the number of clusters.
Step-2: Select random K points or centroids. (It can be other from the input dataset).
Step-3: Assign each data point to their closest centroid, which will form the predefined K clusters.
Step-4: Calculate the variance and place a new centroid of each cluster.
Step-5: Repeat the third steps, which means reassign each datapoint to the new closest centroid of each cluster.
Step-6: If any reassignment occurs, then go to step-4 else go to FINISH.
Step-7: The model is ready.
Usecase Of K-means : —
1. insurance 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. check out this white paper on using clustering in automobile insurance to detect frauds.
2. identifying crime localities
with data related to crimes available in specific localities in a city, the category of crime, the area of the crime, and the association between the two can give quality insight into crime-prone areas within a city or a locality. here is an interesting paper based on crime data from Delhi firms.
3. 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. here is an interesting white paper on how to cyber-profile users in an academic environment based on user data preferences.