Abstract
We propose a new type of regression rules to represent the conditional functional relationship between a response variable and p numericvalued explanatory variables, conditioning on values of a set of categorical variables. Regression rules are ideal for representing relationships existed in mixture of categorical data and numeric data. A set of regression rules can also be presented in the form of a tree graph, called the regression tree, to assist understanding, interpreting, and applying these rules. We also introduce a process for mining regression rules from data stored in a relational database. This process uses the concept of multivariate and multidimensional OLAP to minimize operations for source data retrieval, and uses homogeneity tests to reduce the size of search space. Thus, it can be used to support mining regression rules in an efficient manner in the context of very large databases.
This work was supported by the National Science Council, Republic of China, under Contract NSC 87-2416-H-145-001.
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Sher, BY., Shao, SC., Hsieh, WS. (1998). Mining regression rules and regression trees. In: Wu, X., Kotagiri, R., Korb, K.B. (eds) Research and Development in Knowledge Discovery and Data Mining. PAKDD 1998. Lecture Notes in Computer Science, vol 1394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64383-4_23
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DOI: https://doi.org/10.1007/3-540-64383-4_23
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