This repository contains the source code and article for understanding and implementing K-Means Clustering using C# with an example flower dataset from the Microsoft Learn article on Clustering. The article provides a step-by-step guide, and the code allows you to experiment with K-Means Clustering and data visualization in Google Colab.
Clustering is an unsupervised machine learning technique that groups data points based on their similarities. K-Means Clustering, a popular clustering algorithm, helps reveal patterns and relationships within datasets. This project guides you through K-Means Clustering in C# with a flower dataset, emphasizing data visualization using Google Colab.
To set up your development environment for this project, follow these steps:
git clone https://github.com/adamstirtan/KMeansClustering
cd KMeansClustering
dotnet run
The code for implementing K-Means Clustering in C# can be found in this repository. You can explore the step-by-step implementation, load the flower dataset, preprocess the data, and perform clustering. Feel free to customize and experiment with the code. Google Colab provides a user-friendly environment for data visualization. In the article, we explain how to use Google Colab to visualize the clusters created by K-Means Clustering. You can import the clustered data, create scatter plots, and interpret the results.
Read more about visualizing the clusters in Google Colab
You can use this code and article as a starting point for your own clustering projects. Follow the steps provided in the article to implement K-Means Clustering with your dataset and visualize the results using Google Colab.
Happy Clustering!