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Interactive Rule Interestingness Learning

Data mining is the efficient discovery of patterns in large databases, and classification rules are perhaps the most important type of patterns in data mining applications. However, the number of such classification rules is generally too huge that selection of interesting ones among all discovered rules becomes an important task. In this paper, factors related to the interestingness of a rule are investigated and some new factors are proposed. Following this, an interactive rule interestingness-learning algorithm (IRIL) is developed to automatically label the classification rules either as “interesting” or “uninteresting” with limited user participation. In our study, VFPI (Voting Feature Point Intervals), a feature projection based concept description learning and classification algorithm, is also developed in the framework of IRIL. Being specific to our concerns, VFPI takes the rule interestingness factors as features and is used to learn the rule interestingness concept and to classify the unlabeled classification rules. The success of the previously developed feature projection based learning and classification techniques encouraged us to develop VFPI. The empirical results based on TCMB (Central Bank of Turkish Republic) data set give promising results.