Interval-valued centroids in K-Means algorithms

Benjamine Nordin, Chenyi Hu, Bernard Chen, Victor S. Sheng

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

3 Scopus citations

Abstract

The K-Means algorithms are fundamental in machine learning and data mining. In this study, we investigate interval-valued rather than commonly used point-valued centroids in the K-Means algorithm. Using a proposed interval peak method to select initial interval centroids, we have obtained overall quality improvement of clusters on a set of test problems in the Fundamental Clustering Problem Suite (FCPS).

Original languageEnglish
Title of host publicationProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Pages478-481
Number of pages4
DOIs
StatePublished - 2012
Event11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 - Boca Raton, FL, United States
Duration: Dec 12 2012Dec 15 2012

Publication series

NameProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Volume1

Conference

Conference11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
Country/TerritoryUnited States
CityBoca Raton, FL
Period12/12/1212/15/12

Keywords

  • Clustering
  • K-Means
  • interval computing

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