Autonomous and deterministic supervised fuzzy clustering

Kian Ming Lim, Chu Kiong Loo, Way Soong Lim

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)


A fuzzy model based on an enhanced supervised fuzzy clustering algorithm is presented in this paper. The supervised fuzzy clustering algorithm [6] allows each rule to represent more than one output with different probabilities for each output. This algorithm implements k-means to initialize the fuzzy model. However, the main drawbacks of this approach are that the number of clusters is unknown and the initial positions of clusters are randomly generated. In this work, the initialization is done by the global k-means algorithm [1], which can autonomously determine the actual number of clusters needed and give a deterministic clustering result. In addition, the fast global k-means algorithm [1] is presented to improve the computation time. The model is tested on medical diagnosis benchmark data and Westland vibration data. The results obtained show that the model that uses the global k-means clustering algorithm [1] has higher accuracy when compared to a model that uses the k-means clustering algorithm. Besides that, the fast global k-means algorithm [1] also improved the computation time without degrading much the model performance.

Original languageEnglish
Pages (from-to)705-721
Number of pages17
JournalNeural Network World
Issue number6
Publication statusPublished - 2010
Externally publishedYes


  • Fast global k-means
  • Fault diagnosis
  • Global k-means
  • Supervised fuzzy clustering

ASJC Scopus subject areas

  • Software
  • General Neuroscience
  • Hardware and Architecture
  • Artificial Intelligence


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