Difference between revisions of "Hierarchical Clustering"

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|Has title=Hierarchical Clustering

Revision as of 13:34, 21 September 2020


Project
Hierarchical Clustering
Project logo 02.png
Project Information
Has title Hierarchical Clustering
Has owner Kyran Adams, Oliver Chang
Has start date 12/1/2017
Has deadline date
Has keywords Cluster, Clustering, Circles, Pain in the ass, Agglomeration
Has project status Active
Has sponsor McNair Center
Copyright © 2019 edegan.com. All Rights Reserved.


Summary

The code is in

E:\projects\hca

The python3 file is main.py

The code uses the AgglomerativeClustering from sklearn.cluster, which doesn't have GPU support.

If this is being run on a new build box then

pip install statistics
pip install gmplot

The input is a tdt file named CoLevelForCircles.txt with 7 columns:

city state year lat lon coname datefirstinv

The output is a tdt file named Results.tsv with 8 columns:

(city, state, year) layer cluster ('lat','long','coname','datefirstinv')

Documentation

There's useful reference material here: https://stackabuse.com/hierarchical-clustering-with-python-and-scikit-learn/

Note that it should be possible to use Tensorflow's KMeansClustering to achieve the same result.

Old Code Notes

This code takes a CoLevel master file, clusters points using k (number of clusters) in the range [1, num points / 5), and creates a file output.tsv.

Output.tsv has columns place, statecode, year, layer, cluster, lat, long, coname, datefirstinv. Layer is k, and cluster is the id of the cluster that the point belongs to.

The original version by Kyran and Oliver is in:

E:\McNair\Projects\FastCircles\src

You can run this program with:

python3 main.py