kpeaks: an R package for quick selection of k for cluster analysis

Zeynel Cebeci, Cagatay Cebeci

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Abstract

The argument k is a mandatory user-specified input argument for the number of clusters which is required to start all of the partitioning clustering algorithms. In unsupervised learning applications, an optimal value of this argument is generally determined by using any of the internal validity indexes. However, the determination of k with aid of these indexes are computationally very expensive because they compute a k value using the results after several runs of a clustering algorithm. On the contrary, the package 'kpeaks' enables to estimate k before starting a clustering session. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in datasets. In this paper, we introduce and illustrate the details of R package 'kpeaks' as an implementation for quick selection of the number of clusters for starting cluster algorithms.

Original languageEnglish
Title of host publication2018 International Conference on Artificial Intelligence and Data Processing - Proceedings
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages7
ISBN (Print)9781538668788
DOIs
Publication statusPublished - 24 Jan 2019
Event2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018 - Malatya, Turkey
Duration: 28 Sep 201830 Sep 2018

Conference

Conference2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018
CountryTurkey
CityMalatya
Period28/09/1830/09/18

Keywords

  • cluster analysis
  • number of clusters
  • partitioning clustering
  • unsupervised learning

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  • Cite this

    Cebeci, Z., & Cebeci, C. (2019). kpeaks: an R package for quick selection of k for cluster analysis. In 2018 International Conference on Artificial Intelligence and Data Processing - Proceedings [8620896] IEEE. https://doi.org/10.1109/IDAP.2018.8620896