Algorithms for Finding Multivariate Discriminant Rules for Classification and Regression Trees

Progress in technologies for data input, such as POS (Point Of Sales) systems, and technologies for data storage, such as high density magnetic or optical recording devices, have made it easier for enterprises to collect massive amounts of data and to store them on hard disk at a very low cost. From the early 90’s, many enterprises have been interested in extracting previously unnoticed information that inspires new marketing strategies from these huge databases. Tech-nologies for extracting such information, or knowledge, from huge databases are called “data mining.” Data mining covers technologies for association analysis, classification and regression, cluster analysis, and evolution analysis. Most of these have been widely studied in the field of databases, statistics, and machine learning. Data mining, in general, is focusing on efficiency so that we can handle emerging huge databases whose size is too large to be processed by the conventional techniques. Among these technologies, the author focused on the association analysis and the classifica-tion and regression in this dissertation. The author considered association rules on numerica