The demo code was written using the Anaconda 4.1.1 distribution (Python 3.5.2 and NumPy 1.14.0), but there are no significant dependencies so any Python 3x and NumPy 1x versions should work. The ...

To run our main training and plotting code, run conda create -n cat-clustering --file requirements.txt in the main directory. We calculate the embeddings of our dataset prior to training to save ...

This repository contains a C++ implementation of the K-means clustering algorithm for driver data analysis. It also includes a Python script for visualizing the clustered data. The C++ code ...

Clustering is also extremely extensive in practical applications, such as: market segmentation, social network analysis, organized computing clusters, and astronomical data analysis. This paper is my ...