Data Mining - A KMeans implementation from Scratch (no Sci-kit)

Getting Started

• Ensure you are have Python3 installed with libraries scipy, numpy, and pandas.

Included Files

• iris.arff: This is the test data file that the kmeans clustering runs on.
• kmeans.py: This is the file that contains the main script to run the clustering on the iris dataset.
• parsing.py: This is the python file used to read in the data from the arff file.
• Assignment-3-Report.pdf: This is the file that contains our overall report and contribution sheet.

Usage

• To run the clustering, execute the following command: python3 kmeans.py <k_value> <epsilon_value> <number_of_iterations>
  • k_value is the number of clusters the program will produce.
  • epsilon_value is the change in the sum of the distances from the cluster centers.
  • number_of_iterations is the number of iterations the program will go through to find the best clustering.
• To run finding and plotting the optimal k values of the dataset, uncomment line 184 in kmeans.py