K-Means Clustering: Everything You Need to Know

K-Means Clustering: Everything You Need to Know

Everything you need to know about K-means clustering. This article will be the best guide for you which explains how K-Means Clustering works, how to measure the quality of clusters, choose the optimal number of K.

Data is essential for data science. With a lot of data being generated every second, it’s no surprise that most of this data is unlabeled. But this is okay because there are different techniques, to handle unlabeled datasets. Even there’s an entire domain of Machine Learning called “Unsupervised Learning” that deals with unlabeled data.

Sometimes we want to see how the data is organized, and that’s where clustering comes into play. Though it’s mostly used for unlabeled data, it works fine for labeled data as well. The word ‘clustering’ means grouping similar items together. The most commonly used clustering method is K-Means.

This article will be the best guide for you which explains how K-Means Clustering works, how to measure the quality of clusters, choose the optimal number of K.





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