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Genetic Algorithm on K-Means Clustering

This Project is mainly based on the Genetic-Kmeans-Algorithm-GKA-

The approaches which I used

  • Min-max normalization for standardization
  • Davies–Bouldin index for evaluation of each cluster
  • IN GENETIC :
    • Rank-based selection
    • One-point crossover

Requirements

  • Panda
  • NumPy

Getting Started

python __main__.py

Input

  • The data that I analyzed is from Iris
    • data/iris.csv have 3 column and data/iris2.csv have 4 column and data/isis_with_header.csv with header
  • config.txt contain control parameters
    • kmax: maximum number of clusters
    • budget: budget of how many times run GA
    • numOInd: number of Individual
    • Ps: the probability of ranking Selection
    • Pc: the probability of crossover
    • Pm: the probability of mutation

Output

  • norm_data.csv is normalization data
  • cluster_json is centroid of each cluster
  • result.csv is data with labeled to each cluster

Analysis

  • the accuracy of GA on K-means: 88%
  • the accuracy of k-means++: 83%

Reference