Tackling Initial Centroid of K-Means with Distance Part (DP-KMeans)

Ahmad Ilham; Danny Ibrahim; Luqman Assaffat; Achmad Solichan

Tackling Initial Centroid of K-Means with Distance Part (DP-KMeans)

Machine Learning 2019-03-20 v1

Authors: Ahmad Ilham , Danny Ibrahim , Luqman Assaffat , Achmad Solichan

Abstract

The initial centroid is a fairly challenging problem in the k-means method because it can affect the clustering results. In addition, choosing the starting centroid of the cluster is not always appropriate, especially, when the number of groups increases.

Keywords

k-means initial centroid clustering distance part dp-kmeans

Cite

@article{arxiv.1903.07977,
  title  = {Tackling Initial Centroid of K-Means with Distance Part (DP-KMeans)},
  author = {Ahmad Ilham and Danny Ibrahim and Luqman Assaffat and Achmad Solichan},
  journal= {arXiv preprint arXiv:1903.07977},
  year   = {2019}
}

Comments

This paper was presented at Proceeding of 2018 International Symposium on Advanced Intelligent Informatics (SAIN), 29-30 August 2018, Yogyakarta, Indonesia

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