On Various Types of Even-Sized Clustering Based on Optimization

  • Yasunori Endo
  • Tsubasa Hirano
  • Naohiko Kinoshita
  • Yikihiro Hamasuna
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9880)

Abstract

Clustering is a very useful tool of data mining. A clustering method which is referred to as K-member clustering is to classify a dataset into some clusters of which the size is more than a given constant K. The K-member clustering is useful and it is applied to many applications. Naturally, clustering methods to classify a dataset into some even-sized clusters can be considered and some even-sized clustering methods have been proposed. However, conventional even-sized clustering methods often output inadequate results. One of the reasons is that they are not based on optimization. Therefore, we proposed Even-sized Clustering Based on Optimization (ECBO) in our previous study. The simplex method is used to calculate the belongingness of each object to clusters in ECBO. In this study, ECBO is extended by introducing some ideas which were introduced in k-means or fuzzy c-means to improve problems of initial-value dependence, robustness against outliers, calculation cost, and nonlinear boundaries of clusters. Moreover, we reconsider the relation between the dataset size, the cluster number, and K in ECBO.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Yasunori Endo
    • 1
  • Tsubasa Hirano
    • 2
  • Naohiko Kinoshita
    • 3
  • Yikihiro Hamasuna
    • 4
  1. 1.Faculty of Engineering, Information and SystemsUniversity of TsukubaTsukubaJapan
  2. 2.Canon Inc.Ota-kuJapan
  3. 3.Research Fellowship for Young Scientists of JSPSUniversity of TsukubaTsukubaJapan
  4. 4.Department of InformaticsKindai UniversityHigashiosakaJapan

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