Title :
Supervised and unsupervised data mining with an evolutionary algorithm
Author :
Cattral, Robert ; Oppacher, Franz ; Deugo, Dwight
Author_Institution :
Sch. of Comput. Sci., Carleton Univ., Ottawa, Ont., Canada
Abstract :
This paper describes our current research with RAGA (Rule Acquisition with a Genetic Algorithm). RAGA is a genetic algorithm and genetic programming hybrid that is designed for the tasks of supervised and certain types of unsupervised data mining. Since its initial release we have improved its predictive accuracy and data coverage, as well as its ability to generate more scalable rule hierarchies. These enhancements and several experiments are described
Keywords :
data mining; genetic algorithms; unsupervised learning; RAGA; Rule Acquisition with a Genetic Algorithm; data coverage; evolutionary algorithm; genetic algorithm; genetic programming; knowledge discovery in databases; predictive accuracy; supervised data mining; unsupervised data mining; Computer science; Data mining; Databases; Evolutionary computation; Genetic algorithms; Genetic programming; Hybrid intelligent systems; Supervised learning; Testing; Unsupervised learning;
Conference_Titel :
Evolutionary Computation, 2001. Proceedings of the 2001 Congress on
Conference_Location :
Seoul
Print_ISBN :
0-7803-6657-3
DOI :
10.1109/CEC.2001.934267
