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Data clustering using multivariant optimization algorithm

  • Qin-Hu Zhang
  • Bao-Lei Li
  • Ya-Jie Liu
  • Lian Gao
  • Lan-Juan Liu
  • Xin-Ling ShiEmail author
Original Article

Abstract

Data clustering is one of the most popular techniques in data mining to group data with great similarity and high dissimilarity into each cluster. This paper presents a new clustering method based on a novel heuristic optimization algorithm proposed recently and named as multivariant optimization algorithm (MOA) to locate the optimal solution automatically through global and local alternating search implemented by a global exploration group and several local exploitation groups. In order to demonstrate the performance of MOA-clustering method, it is applied to group six real-life datasets to obtain their clustering results, which may be compared with those received by employing K-means algorithm, genetic algorithm and particle swarm optimization. The results show that the proposed clustering algorithm is an effective and feasible method to reach a high accurate rate and stability in clustering problems.

Keywords

Data clustering Cluster centers Multivariant optimization algorithm Global and local optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Qin-Hu Zhang
    • 1
  • Bao-Lei Li
    • 1
  • Ya-Jie Liu
    • 1
  • Lian Gao
    • 1
  • Lan-Juan Liu
    • 1
  • Xin-Ling Shi
    • 1
    Email author
  1. 1.Department of Electronic Engineering, School of InformationYunnan UniversityKunmingChina

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