TY - JFULL AU - Luciano Nieddu and Giuseppe Manfredi and Salvatore D'Acunto and Katia La Regina PY - 2011/4/ TI - A Optimal Subclass Detection Method for Credit Scoring T2 - International Journal of Economics and Management Engineering SP - 339 EP - 345 VL - 5 SN - 1307-6892 UR - https://publications.waset.org/pdf/51 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 51, 2011 N2 - In this paper a non-parametric statistical pattern recognition algorithm for the problem of credit scoring will be presented. The proposed algorithm is based on a clustering k- means algorithm and allows for the determination of subclasses of homogenous elements in the data. The algorithm will be tested on two benchmark datasets and its performance compared with other well known pattern recognition algorithm for credit scoring. ER -