Fully unsupervised clustering in nonlinearly separable data using intelligent Kernel K-Means

Teny Handhayani, Ito Wasito

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

Intelligent Kernel K-Means is a fully unsupervised clustering technique. This technique is developed by combining Intelligent K-Means and Kernel K-Means. Intelligent Kernel K-Means used to cluster kernel matrix without any information about the number of clusters. The goal of this research is to evaluate the performance of Intelligent Kernel K-Means for clustering nonlinearly separable data. Various artificial nonlinearly separable data are used in this experiment. The best result is the clustering often ring datasets. It produces Adjusted Rand Index (ARI) = 1.

Original languageEnglish
Title of host publicationProceedings - ICACSIS 2014
Subtitle of host publication2014 International Conference on Advanced Computer Science and Information Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages450-453
Number of pages4
ISBN (Electronic)9781479980758
DOIs
Publication statusPublished - 23 Mar 2014
Event2014 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2014 - Jakarta, Indonesia
Duration: 18 Oct 201419 Oct 2014

Publication series

NameProceedings - ICACSIS 2014: 2014 International Conference on Advanced Computer Science and Information Systems

Conference

Conference2014 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2014
CountryIndonesia
CityJakarta
Period18/10/1419/10/14

Keywords

  • clustering
  • fully unsupervised clustering
  • intelligent Kernel K-Means
  • K-Means

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