Inducer incorporates several variants of two well-known rule generation algorithms: TDIDT (which produces classification rules in the intermediate form of a classification tree) and Prism (which generates modular rules directly). Further information about these algorithms is available from the publications listed below.
Publications on TDIDT
Quinlan, J.R. (1986). Induction of Decision Trees. Machine Learning, 1, pp. 81-106
Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann
Bramer, M.A. (1997). Rule Induction in Data Mining: Concepts and Pitfalls. Data Warehouse Report, No. 10, pp. 11-17 and No. 11, pp. 22-27
Publications on Prism
Cendrowska, J. (1987). PRISM: an Algorithm for Inducing Modular Rules. International Journal of Man-Machine Studies, 27, pp. 349-370
Bramer, M.A. (2000). Automatic Induction of Classification Rules from Examples Using N-Prism. In: Research and Development in Intelligent Systems XVI. Springer-Verlag, pp. 99-121
Bramer, M.A. (2002). An Information-Theoretic Approach to the Pre-pruning of Classification Rules. Proceedings of the IFIP World Computer Congress, Montreal, 2002.