Two Stage Fuzzy Clustering Based on Latent Knowledge Discovery and Its Application in the Credit Market

The aim of this paper is to adopt two-stage classification methods, and to apply fuzzy clustering analysis for mining data in the credit market in order to reflect the characteristic type knowledge of common nature of the similar things and different type characteristic knowledge of dissimilar things. First of all, the paper carries on attribute normalization of multi-factors which influence banks credit, computes fuzzy analogical relation coefficient, sets the threshold level to alpha by considering the competition and social credit risks state in the credit market, and selects borrowers through transfer closure algorithm. Second, it makes initial classification on samples according to the coefficient characteristic of fuzzy relation; third, it improves fuzzy clustering method which the fussy clustering itself has fuzzy nature and the algorithm. Finally the paper provides a case study about knowledge of credit mining in the financial market