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
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.
