Autonomous and deterministic supervised fuzzy clustering with data imputation capabilities

A fuzzy model based on enhanced supervised fuzzy clustering algorithm is presented in this paper. Supervised fuzzy clustering algorithm by Janos Abonyi and Ferenc Szeifert in the year 2003 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. The main drawbacks of this approach are the number of clusters is unknown and the initial positions of clusters are randomly generated. In this work, the initialization is performed by global k-means algorithm [1] which can autonomously determine the actual number of clusters needed and give deterministic clustering result. In addition, fast global k-means [1] is presented to improve the computation time. Besides that, when collecting input data in a feature vector way, it might occur that some of the feature values are lost for a particular vector due to a faulty reading sensor. To deal with missing values in enhanced supervised fuzzy clustering, the efficient way is imputation during data preprocessing. The modified of optimal completion strategy is presented to solve this problem. This method allows imputation of missing data with high reliability and accuracy. The autonomous and deterministic enhanced supervised fuzzy clustering using supervised Gath-Geva clustering method and the modified of optimal completion strategy can be derived from the unsupervised Gath-Geva algorithm. The proposed algorithm is successfully justified based on benchmark data sets and a real vibration data which was collected from U.S. Navy CH-46E helicopter aft gearbox called Westland.

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