surya-murali / clustering-using-kmeans

This project uses K-means algorithm to partition a set of points into K clusters. The algorithm works iteratively to assign each data point to one of K groups based on the features provided.

Clustering-Using-KMeans

K-means is an unsupervised clustering algorithm that tries to partition a set of points into k clusters. It is used when you have to group a collection of stuff into various clusters.

The Algorithm:

• Assign random positions for k centroids
• Compute the distance of each point from the centroids and assign each point to its nearest centroid, thereby forming k clusters
• Take the mean of the distance of the points assigned to each centroid. This now becomes the positions of the new centroids
• Now check the error(distance) between the positions of old and new centroids.
• If the error is not equal to 0, repeat steps 3 and 4. If the positions of the old and new centroids match, then the required clusters are formed!

This gif might help you better understand the algorithm

However, using Python's Scikit to perform KMeans is much simpler The outputs can be found here.

Some practical applications:

• Pricing Segmentation
• Customer Need Segmentation
• Loyalty Segmentation
• Where do millionaires live?
• Create stereotypes from demographics data