AUTONOMOUS AND DETERMINISTIC SUPERVISED FUZZY CLUSTERING by ProQuest

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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. [PUBLICATION ABSTRACT]

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									      AUTONOMOUS AND DETERMINISTIC
          SUPERVISED FUZZY CLUSTERING
                 Kian Ming Lim, Chu Kiong Loo, Way Soong Lim∗




Abstract: A fuzzy model based on an enhanced supervised fuzzy clustering al-
gorithm 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
								
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