Abstract. In this paper, we analyze quantitative measures associated with if-then type rules. Basic quantities are identified and many existing measures are examined using the basic quantities. The main objective is to provide a synthesis of existing results in a simple and unified framework. The quantitative measure is viewed as a multi-facet concept, representing the confidence, uncertainty, applicability, quality, accuracy, and interestingness of rules. Roughly, they may be classified as representing one-way and two-way supports. 1

Abstract- In data mining, nugget discovery is the discovery of interesting classification rules that apply to a target class. In previous research, heuristic methods (Genetic algorithms, Simulated Annealing and Tabu Search) have been used to optimise a single measure of interest. This paper proposes the use of multiobjective optimisation evolutionary algorithms to allow the user to interactively select a number of interest measures and deliver the best nuggets (an approximation to the Pareto-optimal set) according to those measures. Initial experiments are conducted on a number of databases, using an implementation of the Fast Elitist Non-Dominated Sorting Genetic Algorithm (NSGA), and two well known measures of interest. Comparisons with the results obtained using modern heuristic methods are presented. Results indicate the potential of multi-objective evolutionary algorithms for the task of nugget discovery. 1

It is well-known that no classification algorithm is the best in all application domains. The conventional approach for coping with this problem consists of trying to select the best classification algorithm for the target application domain. We propose a refreshing departure from this ∗Corresponding author 1 approach, consisting of automatically creating a rule induction algorithm tailored to the target application domain. This work proposes a grammarbased genetic programming (GGP) system to perform “algorithm construction”. The GGP is used to build a complete rule induction algorithm tailored to 5 well-known UCI data sets and a protein data set, where the goal is to predict whether or not a protein presents postsynaptic activity. The results show that the rule induction algorithms automatically constructed by the GGP are competitive with well-known human-designed rule induction algorithms. Moreover, in the postsynaptic case study, the GGP was more successful than the human-designed algorithms in discovering accurate rules predicting the minority class – whose prediction is more difficult and tends to be more important to the user than the prediction of the majority class.