Fuzzy kernel K-medoids algorithm for multiclass multidimensional data classification

Zuherman Rustam, Aini Suri Talita

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


The success of the classification method is highly dependent on how to specify initial data as the initial prototype, dissimilarity functions that we used and the presence of outliers among the data. To overcome these obstacles, in this paper we present Fuzzy Kernel k-Medoids (FKkM) algorithm that we claim to be robust against outliers, invariant under translation and data transformation, as the combined development of Fuzzy LVQ, Fuzzy k-Medoids and Kernel Function. Based on the experiments, it provides a better accuracy than Support Vector Machines, Kernel Fisher Discriminant and RBF Neural Network for multiclass multidimensional data classification.

Original languageEnglish
Pages (from-to)147-151
Number of pages5
JournalJournal of Theoretical and Applied Information Technology
Issue number1
Publication statusPublished - 1 Jan 2015


  • Classification
  • Fuzzy K-medoids
  • Fuzzy LVQ
  • Kernel function
  • Multiclass multidimensional data

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