An analysis of the application of simplified silhouette to the evaluation of k-means clustering validity

Fei Wang, Hector Hugo Franco-Penya, John D. Kelleher, John Pugh, Robert Ross

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

68 Citations (Scopus)

Abstract

This paper analyses the application of Simplified Silhouette to the evaluation of k-means clustering validity and compares it with the k-means Cost Function and the original Silhouette. We conclude that for a given dataset the k-means Cost Function is the most valid and efficient measure in the evaluation of the validity of k-means clustering with the same k value, but that Simplified Silhouette is more suitable than the original Silhouette in the selection of the best result from kmeans clustering with different k values.

Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 13th International Conference, MLDM 2017, Proceedings
EditorsPetra Perner
PublisherSpringer Verlag
Pages291-305
Number of pages15
ISBN (Print)9783319624150
DOIs
Publication statusPublished - 2017
Event13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017 - New York, United States
Duration: 15 Jul 201720 Jul 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10358 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2017
Country/TerritoryUnited States
CityNew York
Period15/07/1720/07/17

Keywords

  • Cost function
  • K-means
  • Silhouette
  • Simplified silhouette

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