README.md

CPSC 473 Term Project: Voronoi Diagram and Voronoi k-Distance

Setting Up and Running

Dependencies:

• pandas
• numpy
• matplotlib
• scipy
• sklearn

Voronoi Diagram Generation

To test our home-grown implementation of the Voronoi diagram generation algorithm, run python diagram_generation.py from the project root directory. Since this is simply a proof-of-concept, no input need be provided: it will generate a set of random datapoints and plot the corresponding Voronoi diagram. Note that our implementation is flawed, so the diagram is occasionally missing edges for some inputs.

Voronoi k-Distance Outlier Detection

The main component of the project is the efficient density-based outlier detection algorithm we implemented. To try it, run python voronoi-k-distance.py followed by the dataset you wish to detect outliers in. For example, for ahmedabad.csv, one would run python voronoi-k-distance.py ./datasets/amhedabad.csv.

Optionally, the desired value of k can be provided via a second argument. For example, to find outliers for clusters.csv via voronoi 2-distance, one would run python voronoi-k-distance.py ./datasets/clusters.csv 2. If no second argument is provided, the 3-distance is used by default.

One can (and should) take the time to play with different datasets and values of k. For a beginner, clusters.csv and ahmedabad.csv are good datasets to start with.

An output file, output.csv, is created in the project root directory whenever the program is run. The output file format is described in the formal report.