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The intestinal microbiota compositions of 92 men living in Japan were identified following consumption of identical meals for 3 days. Fecal samples were analyzed by terminal restriction fragment length polymorphism with 4 primer-restriction enzyme systems, and the 120 obtained operational taxonomic units (OTUs) were analyzed by Data mining software focusing on the following 5 characteristics, namely, age, body mass index, present smoking habit, cessation period of previous smokers and drinking habit, according to the answers of the subjects. After performing Data mining analyses with each characteristic, the details of the constructed Decision trees precisely identified the subjects or discriminated them into various suitable groups. Through the pathways to reach the groups, practical roles of the related OTUs and their quantities were clearly recognized. Compared with the other identification methods for OTUs such as bicluster analyses, correlation coefficients and principal component analyses, the clear difference of this Data mining technique was that it set aside most OTUs and emphasized only some closely related ones. For example for a selected characteristic, such as smoking habit, only 7 OTUs out of 120 were able to identify all smokers, and the remaining 113 OTUs were thought of as data noise for smoking. Data mining analyses were affirmed as an effective method of subject discrimination for various physiological constitutions. The species of bacteria that were closely related to heavy smokers, i.e., HaeIII-291, were also discussed.