K-Means Clustering Use Case

Clustering?is one of the most common exploratory data analysis technique used to get an intuition about the structure of the data. It can be defined as the task of identifying subgroups in the data such that data points in the same subgroup (cluster) are very similar while data points in different clusters are very different. In other words, we try to find homogeneous subgroups within the data such that data points in each cluster are as similar as possible according to a similarity measure such as euclidean-based distance or correlation-based distance. The decision of which similarity measure to use is application-specific.

Unlike supervised learning, clustering is considered an unsupervised learning method since we don’t have the ground truth to compare the output of the clustering algorithm to the true labels to evaluate its performance. We only want to try to investigate the structure of the data by grouping the data points into distinct subgroups.

Where do we apply k-means?

k-means can typically be applied to data that has a smaller number of dimensions, is numeric, and is continuous. think of a scenario in which you want to make groups of similar things from a randomly distributed collection of things; k-means is very suitable for such scenarios.

Here is a list of ten interesting use cases for k-means.

• document classification
• fantasy league stat analysis
• cyber-profiling criminals
• automatic clustering of it alerts
• call record detail analysis
• delivery store optimization

Applications

kmeans algorithm is very popular and used in a variety of applications such as market segmentation, document clustering, image segmentation, and image compression, etc.?