How to Scale K-Means Clustering with just ClickHouse SQL

This article provides a detailed walkthrough on how to perform K-Means clustering using SQL queries with ClickHouse, an open-source columnar database management system. The author explains the theory behind K-Means clustering and demonstrates its implementation in SQL. They also discuss feature selection, choosing the optimal value of K, and visualizing the clusters formed. The article includes a sample dataset from NYC taxis and provides code snippets for performing various operations related to K-Means clustering. The author also compares the performance of their ClickHouse implementation with scikit-learn, a popular machine learning library in Python, on a larger dataset. Overall, this article is an excellent resource for anyone interested in implementing K-Means clustering using SQL queries and provides valuable insights into various aspects of the algorithm.