Implementation of a text clustering algorithm using Kmeans clustering in order to derive quick insights from unstructured text
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README.md

Text-Clustering-API

Implementation of a text clustering algorithm using Kmeans clustering in order to derive quick insights from unstructured text. Please check the below links for details -

Docker Setup

  1. Install Docker
  2. Run git clone https://github.com/vivekkalyanarangan30/Text-Clustering-API
  3. Open docker terminal and navigate to /path/to/Text-Clustering-API
  4. Run docker build -t clustering-api .
  5. Run docker run -p 8180:8180 clustering-api
  6. Access http://192.168.99.100:8180/apidocs/index.html from your browser [assuming you are on windows and docker-machine has that IP. Otherwise just use localhost]

Native Setup

  1. Anaconda distribution of python 2.7
  2. pip install -r requirements.txt
  3. Some dependencies from nltk (nltk.download() from python console and download averaged perceptron tagger)

Run it

  1. Place the script in any folder
  2. Open command prompt and navigate to that folder
  3. Type "python CLAAS.py"and hit enter
  4. Go over to http://localhost:8180/apidocs/index.html in your browser (preferably Chrome) and start using.