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genetic-algorithm-on-k-means-clustering's Introduction

Genetic Algorithm on K-Means Clustering

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

The approaches which I used

  • Minmax 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

  • data which I analysis them is 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 : probability of ranking Selection
    • Pc : probability of crossover
    • Pm : 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

genetic-algorithm-on-k-means-clustering's People

Contributors

amirdeljouyi avatar

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