Abstract

Clustering is part of data mining. Clustering is used to group objects so that one group has the same characteristics. K-means widely used because it is relatively easy to use. However K-means has shortcomings. K-means depends on the initial centroid. Selection of initial centroid done randomly so that the cluster formed is often not optimal. The clustering results are sometimes good and sometimes bad. In this research, the Genetic K-means Algorithm is used to improve K-means method. Genetic algorithm method is used to find the initial centroid. The initial centroid will be used by K-means. So K-means can get the optimal cluster. Cluster results is validated by SSW (Sum of Square within Cluster) and SI (Silhouette Index). SSW values by Genetic K-means Algorithm amounted 1,648,150,772.8 and K-means amounted 2.390.800.216,39. In this research, it was found that Genetic K-means Algorithm creates a homogenous cluster of 45% better than the K-means. So Genetic K-means Algorithm more accurate than K-means in determining patterns of data.

Original languageEnglish
Title of host publicationProceedings - 2016 International Seminar on Application of Technology for Information and Communication, ISEMANTIC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages278-284
Number of pages7
ISBN (Electronic)9781509023264
DOIs
Publication statusPublished - 7 Mar 2017
Event2016 International Seminar on Application of Technology for Information and Communication, ISEMANTIC 2016 - Semarang, Indonesia
Duration: 5 Aug 20166 Aug 2016

Publication series

NameProceedings - 2016 International Seminar on Application of Technology for Information and Communication, ISEMANTIC 2016

Conference

Conference2016 International Seminar on Application of Technology for Information and Communication, ISEMANTIC 2016
Country/TerritoryIndonesia
CitySemarang
Period5/08/166/08/16

Keywords

  • Cluster
  • Genetic k-means algorithm
  • Sum of square within cluster

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