Rhitabrat/Fuzzy-C-means-from-scratch

Simple implementation of Fuzzy C-means algorithm using python. It is used for soft clustering purpose. Visualizing the algorithm step by step with the cluster plots at each step and also the final clusters.

Fuzzy C-means Algorithm

Type: Clustering algorithm

Dataset used: Iris dataset

Requirements:

• google colab or jupyter notebook

packages:

• Pandas - https://pandas.pydata.org/docs/getting_started/install.html
  
• Numpy - https://numpy.org/install/
  
• Matplotlib - https://matplotlib.org/stable/users/installing.html
  
• sklearn - https://scikit-learn.org/stable/install.html
  

Steps involved:

1. Open the “fuzzy_c_means_algorithm_implementation.ipynb” file.You can either open it on google colab or through jupyter notebook.
2. If you are using Jupyter notebook then install the required packages mentioned above.
3. After opening the file in google colab or jupyter notebook run all the cells and see the output.
4. Observe the plots to understand the results of the algorithm.

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References:

https://www.kaggle.com/prateekk94/fuzzy-c-means-clustering-on-iris-dataset
https://youtu.be/FA-hJBu5Bkc
http://www.cleartheconcepts.com/fuzzy-c-means/