Variable neighborhood search for harmonic means clustering

Abdulrahman Alguwaizani, Pierre Hansen, Nenad Mladenović, Wai Ting Ngai

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)

Abstract

Harmonic means clustering is a variant of minimum sum of squares clustering (which is sometimes called K-means clustering), designed to alleviate the dependance of the results on the choice of the initial solution. In the harmonic means clustering problem, the sum of harmonic averages of the distances from the data points to all cluster centroids is minimized. In this paper, we propose a variable neighborhood search heuristic for solving it. This heuristic has been tested on numerous datasets from the literature. It appears that our results compare favorably with recent ones from tabu search and simulated annealing heuristics.
Original languageEnglish
Pages (from-to)2688-2694
Number of pages7
JournalApplied Mathematical Modelling
Volume35
Issue number6
DOIs
Publication statusPublished - 1 Jun 2011
Externally publishedYes

Keywords

  • Clustering
  • K-harmonic means
  • Metaheuristics
  • Minimum sum of squares
  • Unsupervised learning
  • Variable neighborhood search

ASJC Scopus subject areas

  • Modelling and Simulation
  • Applied Mathematics

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