k-mean clustering and its real usecase in the security domain
In this article you are going to known about what is k-mean clustering , where you can use it and it’s industry usecase in security domain.
When trying to analyze data, one approach might be to look for meaningful groups or clusters. Clustering is dividing data into groups based on similarity. And K-means is one of the most commonly used methods in clustering. It is because it’s simplicity.
How does k-mean clustering works?
Let’s say we’d like to divide the following points into clusters.
step1:choose k value for ex: k=2
step2:initialize centroids randomly
step3:calculate Euclidean distance from centroids to each data point and form clusters that are close to centroids
step4: find the centroid of each cluster and update centroids
step:5 repeat step3
Each time clusters are made centroids are updated, the updated centroid is the center of all points which fall in the cluster. This process continues till the centroid no longer changes i.e solution converges.
How To Choose K Value In K-Means:
1.Elbow method
steps:
step1: compute clustering algorithm for different values of k.
for example k=[1,2,3,4,5,6,7,8,9,10]
step2: for each k calculate the within-cluster sum of squares(WCSS).
step3: plot curve of WCSS according to the number of clusters.
step4: The location of bend in the plot is generally considered an indicator of the approximate number of clusters.
We can also use this concepts of kmeans clustering in cyber security .for example
- Customer Profiling
- Market segmentation
- Computer vision
- Geo-statistics
- Astronomy
- Document clustering
- Identifying crime-prone areas
- Cluster analysis
- Feature learning or dictionary learning
- Identifying crime-prone areas
Thank you for reading this article!!!!!!!
