EGA-FMC: Enhanced Genetic Algorithm based Fuzzy K - Modes Clustering for Categorical Data

We implemented a fuzzy K-Modes clustering algorithm using Genetic Algorithm for categorical data. The algorithm is an extension to the work,"A genetic fuzzy k-Modes algorithm for clustering categorical data".

Outline of the proposed algorithm:

1. Intitialize the chromosomes. Each chromosome is a membership matrix representing a possible solution to the clustering problem.
2. Evaluating the fitness of the chromosomes.
3. Choosing the best parent chromosome.
4. Multi-objective rank-based assignment and Roulette Wheel Selection, Crossover and Mutation operations.
5. Elitism Operation: Replacing the worst child with the best parent.
6. Repeat until termination.
7. Clustering solution is the choromosome with the best value for Arithment Rand Index, Inter-cluster separation, and Intre-cluster distance.

The method outperforms the state-of-the-art algorithms in terms of Arithment Rand Index, Inter-cluster separation, Intre-cluster distance and Computation Time.

Requirments

• Python 2.7 or above

Instructions to run

python ega-fmc.py
python ega-fmc-zoo.py

Members

1. Medhini Narasimhan (medhini95@gmail.com)
2. Suryansh Kumar
3. Balaji Balasubramanian