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# Basics of K means classification- An unsupervised learning algorithm

Ashish R.
18/11/2016 0 0

K-means is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. The procedure follows a simple and easy way to classify a given data set with n objects through a certain number of K clusters. K-means stands for K number of clusters to form in your training sample. The idea behind doing the clustering is that elements that are very much similar with respect to each other with respect to the considered parameters/attributes should go to the same cluster. As a result we could expect that variability within cluster should be very low (as much minimum as possible) and variability a across clusters should be maximum. This is to remember when we say that the elements within a cluster should be very much similar then we articulate the similarity with respect to the considered variables that are used in the execution of the classification algorithm. The elements within a cluster/segment might differ with respect to some other parameters that are not considered in the execution of the algorithm. For example, if we have a 1000 stores from retail chain to cluster them into multiple groups based on the parameters like sales volume, size, # of SKUs available, # of labors deployed then within each cluster the stores they might vary with respect of some parameters like store managers experience, made os payment accepts inside the store etc.

Now it is to know, where it is used or what kind of problems are solved using this technique:

Telecom domain: Segment the customers based on network usage data across various services (Youtube, Google, Social media, Netflix, VPN work etc.). the idea is to cluster the customers so that the right segment of the customers could be trageted with right stratey for product/services upsell and cross sell

Reatil Banking: Segment the credit card applicants based on age, income, occupation, gender and other demographic profile to determine the credit limit.

Insurance domain: Segment the customer base based on age, lifestyle, income, demographic features to determine the insurance premium.

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