Implementation of k-means clustering

Python scripts

The repo consists of three scripts:

• analysis.py: basic functions that are used repetitively in other scripts;
• elbow.py: plots the cost function (elbow method) to find the appropriate number of clusters;
• iteration.py: produces table output of clustering based on the number of clusters determined by the elbow plot.

Notebooks

Notebooks demonstrate how the clustering algorithm defined in the scripts above could be applied

• example.ipynb and new_data_example.ipynb show worked examples of how k-means clustering is implemented for 2D datasets.
• t_shirt.ipynb presents a clustering example of a multi-dimensional dataset.