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README.md

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-# ECAC-S
-
-**Coded by:** Benjamin M. Sainz-Tinajero.  
-**Paper title:** Simoultaneous Evolutionary Clustering using Classifiers and its Application on Satellite Image Segmentation.
-
-ECAC-S is available in this repository in a Python implementation.
-
-# Data Preparation
-This implementation requires one mandatory file to perform clustering. A ``.csv`` file named ``iris_X.csv``, for instance, must contain the features of a dataset with no header, one column per attribute and one row per object. A second optional file could contain ground truth labels in case you're running a benchmark and need to compute the Adjusted RAND Index of a solution against a reference mask or partition. This second file must be named ``iris_y.csv`` (for this example) and must have one column with the same number of objects as the ``iris_X.csv`` file (no header), placing each object into one group. Our algorithm automatically searches for both of this files in the ``\data`` path and computes the Adjusted RAND Index only if it finds ground truth labels in a file as mentioned before. We include 40 publicly available datasets complying with our algorithm's required data format. 
-
-# Hyper-parameter Setting
-``data``: a string with the name of the dataset to be retrieved without the ``_X.csv`` or ``_y.csv`` suffixes.   
-``n_clusters``: integer with the number of required clusters. As an alternative, setting this argument as ``'auto'`` will set the number of clusters found in the ground truth file as ``n_clusters`` (this feature only works if there is a ``_y.csv`` ground truth file in the ``\data`` directory).  
-``pop_size`` (default = 200): population size that is carried along the evolutionary process.   
-``max_gens`` (default = 200): maximum generations of the evolutionary process.    
-``runs`` (default = 10): independent runs of the algorithm.  
-
-For more information on the hyper-parameters and their influence in the evolutionary process, we refer the user to the article in Ref.[1].  
-
-# Setup and Run using Python
-Open your preferred Python interface and follow these commands to generate a clustering using ECAC-S. We will continue using the ``iris`` dataset as an example.  
-
-``>>> from gen import *``  
-``>>> ecacs_run(data='iris', n_clusters=3, pop_size=200, max_gens=200, runs=10)``
-
-Running these commands will execute ECAC-S using the ``iris`` dataset's features with 3 clusters, 200 individuals per population, 200 generations, and 10 independent runs, and will compute the Adjusted RAND Index between the solutions and the reference labels in the ``iris_y.csv`` file. A ``.csv`` file with the clustering and the results is stored in the ``/out`` path.
-
-**Important**: You will need to have previously installed some basic data science packages such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn.
-
-An ``example.py`` file is provided with this example for a more straight-forward approach to using the algorithm.  
-
-I hope ECAC-S is a powerful asset for your data science toolkit,
-
-Benjamin  
-**Email:** sainz@tec.mx, bm.sainz@gmail.com  
-**LinkedIn:** https://www.linkedin.com/in/benjaminmariosainztinajero/
-
-# References
-[1] B. M. Sainz-Tinajero, G. I. Perez-Landa, C. E. Orozco-Mora, D. Otero-Argote, and A. E. Gutierrez-Rodriguez, "Scalable Evolutionary Clustering using Classifiers and its Application on Satellite Image Segmentation," 2022. *Paper under preparation for submission.*   
-[2] B. M. Sainz-Tinajero, A. E. Gutierrez-Rodriguez, H. G. Ceballos and F. J. Cantu-Ortiz, "F1-ECAC: Enhanced Evolutionary Clustering Using an Ensemble of Supervised Classifiers," in IEEE Access, 2021, DOI: 10.1109/ACCESS.2021.3116092.  
-[3] B. M. Sainz-Tinajero, A. E. Gutierrez-Rodriguez, H. G. Ceballos, and F. J. Cantu-Ortiz, “Evolutionary clustering algorithm using supervised classifiers,” in 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021. 10.1109/CEC45853.2021.9504826.