Research on GK Fuzzy Clustering Algorithm Based on Subtractive Clustering
Cheng Jun-jie · Journal of Lanzhou Jiaotong University · 2011
Gustafson-Kessel(GK) algorithm is one of the most widely used fuzzy clustering algorithms.But this algorithm is hypersensitive to the setting of the initial value and is easy to fall into local optimal solution.What's more,GK algorithm requires a given clustering number.It is poor at self-adjustment.In view of the above shortcomings of GK algorithm,we adopt the subtractive clustering algorithm to initialize the GK algorithm,which can reflect the data structure better.Based on the initial value applied by the subtractive clustering algorithm,we adopt the clustering validity function to determine the reasonable clustering number in order to achieve the automatic classification and reasonable clustering partitioning results.Finally,the simulation experiments to the artificial data set and the iris data set demonstrated that the improved algorithm can automatically determine the reasonable clustering number and the clustering correctness increased obviously.