Mini-batch K-means Clustering in Machine Learning

The Mini-batch K-means clustering algorithm is a version of the standard K-means algorithm in machine learning. It uses small, random, fixed-size batches of data to store in memory, and then with each iteration, a random sample of the data is collected and used to update the clusters. If you have never used the Mini-batch K-means algorithm in machine learning, this article is for you. In this article, I will introduce you to the Mini-batch K-means clustering algorithm and its implementation using Python.

Mini-batch K-means Clustering

The Mini-batch K-means clustering algorithm is a version of the K-means algorithm which can be used instead of the K-means algorithm when clustering on huge datasets. Sometimes it performs better than the standard K-means algorithm while working on huge datasets because it doesn’t iterate over the entire dataset. It creates random batches of data to be stored in memory, then a random batch of data is collected on each iteration to update the clusters.

The main advantage of using the Mini-batch K-means algorithm is that it reduces the computational cost of finding a cluster. You may prefer to use the K-means algorithm, but when working on a huge dataset, you should prefer to use the mini-batch approach. If you want to understand the difference between these two algorithms, you should read this research paper.

Mini-batch K-means Clustering using Python

I hope you now have understood what Mini-batch K-means clustering is in machine learning and how it is different from the standard K-means algorithm. To implement it using Python, you can use the Scikit-learn library in Python. So below is how you can implement the mini-batch k-means algorithm by using the Python programming language:

   median_income  latitude  longitude  Cluster
0         8.3252     37.88    -122.23        1
1         8.3014     37.86    -122.22        1
2         7.2574     37.85    -122.24        1
3         5.6431     37.85    -122.25        1
4         3.8462     37.85    -122.25        1
Mini-batch K-means Clustering

Summary

So this is how you can use the mini-batch version of the K-means algorithm on large datasets. It is a version of the K-means algorithm which can be used instead of the K-means algorithm when clustering on huge datasets. It creates random batches of data to be stored in memory, then a random batch of data is collected on each iteration to update the clusters. I hope you liked this article on the Mini-batch K-means algorithm in machine learning and its implementation using Python. Feel free to ask your valuable questions in the comments section below. 

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Aman Kharwal
Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder
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