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Data mining is a new methodology for improving the quality and effectiveness of the business and scientific decision-making process and often uses statistical and machine learning techniques for extracting information. It is well known that a new approach by combining statistical methods and machine learning gains more information compared to the situation when each of them is used separately. In this paper, we combine ID3, an algorithm of decision trees with multinomial logistic regression for segmentation categorical data. This technique is applied in real data for steel production line and the results show that the analysis of data is more effective and informative particularly on ordinal responses.