An automatic rule base generation method for fuzzy pattern recognition with multiphased clustering

This paper presents a new approach for the automatic generation of fuzzy rule bases for pattern recognition. The general idea of the approach is to use and enhance the fuzzy c-means clustering algorithm. The rule base is generated through an iterative feature clustering approach. The automatic extraction of features is repeated until the generated rule base is giving an unequivocal answer. Although the rule base generation method was initally developed for handwritting features the scope of its applicability is much larger. The algorithm was tested with feature vectors up to 130 elements.