5 Interesting use-cases of K-means
MD MOQADDAS Here, I am going to explain clustering, k-means clustering, its use cases
Summary -:)
- What is Unsupervised Learning?
- What is clustering?
- What is k-means clustering?
- Use-Cases in the security domain
1. What is Unsupervised Learning?
In the world of Machine Learning, Machine Training known as machine learning, there are two types of learning in machine training
→ Supervise Learning -: In a simple word, in this type of learning, somebody guide, let's, make definition complex, we have labeled data and data guide us in this learning.
→ Unsupervised Learning -: Here we do have not X and Y, There is non-labeled data to train the machine, this is done by the concept clustering.
2. What is clustering?
In simple words, Clustering is a concept to divide the data into different groups.
I want to give you some extra information, question arises here why we divide the data, we do so that we can differentiate that this data belongs to this particular field, etc., etc...
Let’s assume this is a graph of data Satisfaction vs Loyality
Here you can see there is multiple data or population of data, let’s find how many cluster here.
Note-: Data decide that how many clusters are in the dataset.
See there is 3 cluster which has different specification
Under the umbrella of clustering, K-means comes.
3. What is k-means clustering?
k-means clustering is an efficient way to cluster our data in the machine learning world.
Now the point is how the k-means cluster works
How does k-means alog work?
step1: first separate the cluster and specify each cluster as “k”
step2: Initialize centroid, and randomly selecting k data points for the centroids.
step3: Keep iterating until there is no change to the centroids
→ Compute the sum of the squared distance between data points and all centroids.
→ Assign each data point to the closest cluster
→ Compute the centroids for the clusters by taking the average of all data points that belong to each cluster.
k-means algo applies to a variety of applications like segmentation, document clustering, image segmentation, and image compression, etc.
K-means Use-Cases in the security domain -:)
Some Great use-cases with the little brief given below.
- Insurance fraud detection -: We can isolate new fraud claims using their proximity to clusters that indicate fraudulent patterns.
2. Cyber Profiling Criminals -: cyber-profiling is the process of collecting data from individuals and groups to identify significant correlations. 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.
3. Automatic Clustering of its alerts -: large enterprise infrastructures technology components such as network, storage, or database generate large volumes of alert messages. because alert messages potentially point to operational issues, they must be manually screened for prioritization for downstream processes.
4. 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.
5. Spam filter -: You know the junk mails or messages in your email inbox or message app? It is the place where emails or messages that have been identified as spam by the algorithm
This is all about the K-means and its real use-cases,
MD MOQADDAS
Keywords-: machine learning, k-means, clustering, k-means algo, real use-cases of k-means,
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