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Clustering of distribution feeders with Interactive Visualizations

  • k-means clustering algorithm implementation: to identify 7-10 representative feeders from 700+ distribution feeders
  • The algorithm tests k=7, k=8, k=9 etc.. and depending on the Silhouette values for given k, the best number of representative feeders is chosen.
  • The code includes the Principal Component Analysis (PCA) of the dataset as well as 3-D visualizations of the data. Towards the end, scatter matrix plots are also demonstrated.

How to Run

Prerequisites

You will need a python 3 interpreter with the following packages:

  • jupyter notebook
  • scikit learn
  • scipy
  • matplotlib
  • pandas (optional - if you want to run 3-D visualization and interactive plot part)
  • chart_studio/plotly
  • cufflinks

Running the code

  1. Clone (or download) the repository:

git clone https://github.com/sakshi-mishra/clustering-distribution-feeders.git

  1. Open/Run the jupyter notebook Clustering_of _distribution_feeders_with_Interactive_Visualizations.ipynb

  2. Open/Run the .py file Clustering_with_UserDefinedOptions_csvResults.py

User Instructions (for running Clustering_with_UserDefinedOptions_csvResults.py file):

NOTE: Check that raw values make it into the .csv (not standardardized/normalized values)

Input to the program:

1. names of the csv files (more than 1 file/dataset) can be processed at a time.
    Note: files names shouldn't have spaces in between them, you may fill the spaces with underscores.
2. user-defined transformation method: choose from normalization, standardization and minmax-scaling (based on dataset)
3. number of expected cluster: a range of k values (number of clusters to form) can be given as input. If just one value of k to be tested, then enter a = b

Output:

1. csv file named "input_file_name+transformation+date+data" : contains the labels assinged to all the samples
2. csv file named "input_file_name+transformation+date+metric" : contains various evaluation metircs
3. individual scatter plots of the features (for each cluster)
4. silhouette value plot