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We propose a new network-growing method to accelerate learning and to extract explicit features in complex input patterns. We have so far proposed a new type of network-growing algorithm called greedy network growing algorithm (Kamimura, R, et al., 2002), (Kamimura, R, 2003). By this algorithm, a network can grow gradually by maximizing information on input patterns. In the algorithm, the inverse of the square of the ordinary Euclidean distance between input patterns and connection weights is used to produce competitive unit outputs. When applied to some problems, the method has shown slow learning, and sometimes the method cannot produce a state where information is large enough to produce explicit internal representations. To remedy this shortcoming, we introduce here Minkowski distance between input patterns and connection weights used to produce competitive unit outputs. When the parameter for Minkowski distance is larger, some detailed parts in input patterns can be eliminated, which enables networks to converge faster and to extract main parts of input patterns. We applied our new method to the analysis of some economic data. In the experiment, results confirm that a new method with Minkowski distance can significantly accelerate learning, and clearer features can be extracted.