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.
|Number of pages||8|
|Publication status||Published - Dec 2003|
|Event||2003 IEEE Congress on Evolutionary Computation - Canberra, Australia|
Duration: 8 Dec 2003 → 12 Dec 2003
|Conference||2003 IEEE Congress on Evolutionary Computation|
|Period||8/12/03 → 12/12/03|