A New Method of Selecting K-means Initial Cluster Centers Based on Hotspot Analysis

  • Qu Chen
  • , Hong Yi
  • , Yujie Hu
  • , Xianrui Xu
  • , Xiang Li*
  • *Corresponding author for this work

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

7 Scopus citations

Abstract

The initial cluster centers of traditional K-means algorithm are randomly selected from spatially distributed data samples. This procedure may significantly affect the final clustering outputs and is unable to ensure a high-quality solution. This paper attempts to improve the quality of solution and the efficiency of clustering through selecting initial cluster centers based on hotspot analysis. An algorithm is developed to identify K hotspots as initial cluster centers. The proposed algorithm is compared to three existing methods in our experiments. Our results demonstrate that our method can generate similar but more stable clustering results with less number of iterations than others.

Original languageEnglish
Title of host publicationProceedings - 2018 26th International Conference on Geoinformatics, Geoinformatics 2018
EditorsShixiong Hu, Xinyue Ye, Kun Yang, Hongchao Fan
PublisherIEEE Computer Society
ISBN (Electronic)9781538676196
DOIs
StatePublished - 3 Dec 2018
Event26th International Conference on Geoinformatics, Geoinformatics 2018 - Kunming, China
Duration: 28 Jun 201830 Jun 2018

Publication series

NameInternational Conference on Geoinformatics
Volume2018-June
ISSN (Print)2161-024X
ISSN (Electronic)2161-0258

Conference

Conference26th International Conference on Geoinformatics, Geoinformatics 2018
Country/TerritoryChina
CityKunming
Period28/06/1830/06/18

Keywords

  • Clustering
  • Hotspot analysis
  • Initial cluster centers
  • K-means algorithm

Fingerprint

Dive into the research topics of 'A New Method of Selecting K-means Initial Cluster Centers Based on Hotspot Analysis'. Together they form a unique fingerprint.

Cite this