Fuzzy c-Means Clustering Algorithm for Interval Data
Wenjiang Liu · Jisuanji gongcheng · 2008
An advanced method,Interval Fuzzy c-Means Clustering(IFCM),is proposed based on a new definition of distance between interval data which is based on eigenvalues of interval data. The new method expands handling objects from single value sets to interval sets compared with general FCM algorithm. The simulations included at the end indicate the validity of IFCM of which average distortion is 6.81% lower than a similar interval FCM algorithm. Moreover,IFCM can handle interval data with different weight of eigenvalues according to different requirement. It also can be expanded easily to multidimensional type data for the rationality of the new distance definition.