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Comparison of induced rules based on likelihood estimation

Rule induction methods have been applied to knowledge discovery in databases and data mining, The empirical results obtained show that they are very powerful and that important knowledge has been extracted from datasets. However, comparison and evaluation of rules are based not on statistical evidence but on rather naive indices, such as conditional probabilities and functions of conditional probabilities. In this paper, we introduce two approaches to induced statistical comparison of induced rules. For the statistical evaluation, likelihood ratio test and Fisher's exact test play an important role: likelihood ratio statistic measures statistical information about an information table and it is used to measure the difference between two tables.