Scaling-up multiobjective evolutionary clustering algorithms using stratification

Research output: Indexed journal article Articlepeer-review

7 Citations (Scopus)

Abstract

Multiobjective evolutionary clustering algorithms are based on the optimization of several objective functions that guide the search following a cycle based on evolutionary algorithms. Their capabilities allow them to find better solutions than with conventional clustering algorithms when more than one criterion is necessary to obtain understandable patterns from the data. However, these kind of techniques are expensive in terms of computational time and memory usage, and specific strategies are required to ensure their successful scalability when facing large-scale data sets. This work proposes the application of a data subset approach for scaling-up multiobjective clustering algorithms and it also analyzes the impact of three stratification methods. The experiments show that the use of the proposed data subset approach improves the performance of multiobjective evolutionary clustering algorithms without considerably penalizing the accuracy of the final clustering solution.

Original languageEnglish
Pages (from-to)69-77
Number of pages9
JournalPattern Recognition Letters
Volume93
DOIs
Publication statusPublished - 1 Jul 2017

Keywords

  • Clustering
  • Multiobjective evolutionary algorithms
  • Scaling-Up
  • Stratification

Fingerprint

Dive into the research topics of 'Scaling-up multiobjective evolutionary clustering algorithms using stratification'. Together they form a unique fingerprint.

Cite this