The authors present a learning algorithm of decision tree generation for interval-valued attributes. With regard to range of value, a nominal attribute is not ordered and a continuous-valued attribute is linearly ordered, but the interval-valued attribute is partially ordered. As a generalization of ID3-algorithm on intervals, this algorithm uses minimal information entropy of partitioning to select the extended attributes. The efficiency of the algorithm is improved by analyzing unstable cut points.