Autonomous and deterministic supervised fuzzy clustering
Document Type
Article
Publication Date
1-1-2010
Abstract
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.
Keywords
Fuzzy model, fuzzy clustering algorithm
Divisions
ai
Publication Title
Neural Network World
Volume
20
Issue
6
Additional Information
Lim, Kian Ming Loo, Chu Kiong Lim, Way Soong