K Means Clustering and its use cases

Posted By : Rishabh Jain | 21-Jun-2022

What is k- means?

Clustering is the task of dividing the population or data points into several groups similar that data points in the same groups are more analogous to other data points which are in the same group than those in other groups. in simple words, the end is to insulate groups with analogous traits and assign them into clusters. the thing of the k- means algorithm is to find groups in the data, with the number of groups represented by the variable i.e. k. The algorithm works iteratively to assign each data point to one of the k groups grounded on the features that are handed. in the reference image below, k = 2, and there are two clusters linked from the source dataset.

the labors of executing a k- means on a dataset are defined below:-

1 k centroids for each of the k clusters linked from the dataset.
2 complete datasets are labeled to ensure each data point is assigned to one of the clusters.

Where can I apply k- means?

k- means can generally be applied to data that has a lower number of confines, is numeric, and is nonstop. suppose a script in which you want to make groups of analogous effects from an aimlessly distributed collection of effects; k- means is veritably suitable for similar scripts.
Then is a list of ten intriguing use cases for k- means.

1. document bracket
cluster documents in multiple orders grounded on markers, motifs, and the content of the document. this is a veritably standard bracket problem and k- means is a largely suitable algorithm for this purpose. the original processing of the documents is demanded to represent each document as a vector and uses term frequency to identify generally used terms that help classify the document. the document vectors are also clustered to help identify similarities in document groups.

2. delivery store optimization
optimize the process of good delivery using truck drones by using a combination of k- means to find the optimal number of launch locales and an inheritable algorithm to break the truck route as a traveling salesperson problem.


3. relating crime points
with data related to crimes available in specific points in a megacity, the order of crime, the area of the crime, and the association between the two can give quality sapience into crime-prone areas within a megacity or a position.

4. client segmentation
clustering helps marketers ameliorate their client base, work on target areas, and member guests grounded on purchase history, interests, or exertion monitoring. then's a white paper on how telecom providers can cluster pre-paid guests to identify patterns in terms of plutocrat spent in recharging, transferring SMS, and browsing the internet. the bracket would help the company target specific clusters of guests for specific juggernauts.

5. fantasy league stat analysis
assaying player stats has always been a critical element of the sporting world, and with adding competition, machine literacy has a critical part to play then. as an intriguing exercise, if you would like to produce a fantasy draft platoon and like to identify analogous players grounded on player stats, k- means can be a useful option.

6. insurance fraud discovery
machine literacy has a critical part to play in fraud discovery and has multitudinous operations in the machine, healthcare, and insurance fraud discovery. exercising past literal data on fraudulent claims, it's possible to insulate new claims grounded on their propinquity to clusters that indicate fraudulent patterns. since insurance fraud can potentially have a multi-million bone impact on a company, the capability to descry fraud is pivotal.


7. rideshare data analysis
the intimately available uber lift information dataset provides a large quantum of precious data around business, conveyance time, peak volley points, and more. assaying this data is useful not just in the environment of uber but also in furnishing sapience into civic business patterns and helping us plan for the metropolises of the future.

8. cyber-profiling culprits
cyber-profiling is the process of collecting data from individuals and groups to identify significant relations. the idea of cyber profiling is deduced from felonious biographies, which give information on the disquisition division to classify the types of culprits who were at the crime scene.

9. call record detail analysis
a call detail record( cdr) is the information captured by telecom companies during the call, SMS, and internet exertion of a client. this information provides lesser perceptivity about the client’s requirements when used with client demographics. in this composition, you'll understand how you can cluster client conditioning for 24 hours by using the unsupervised k- means clustering algorithm. it's used to understand parts of guests concerning their operation by hours.

10. automatic clustering of it cautions
large enterprises' structure technology factors similar to network, storehouse, or database induce large volumes of alert dispatches. because alert dispatches potentially point to functional issues, they must be manually screened for prioritization for downstream processes. clustering of data can give sapience into orders of cautions and mean time to repair, and help in failure prognostications.

About Author

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Rishabh Jain

He is a Devops person who loves Automation and have skills in various technologies like Docker, Linux, Kubernetes, Ansible, Terraform and Many More.

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