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 algor...Show MoreMetadata
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 multi-objective 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.
Date of Conference: 08-12 December 2003
Date Added to IEEE Xplore: 24 May 2004
Print ISBN:0-7803-7804-0