TY - GEN
T1 - Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes
AU - Fister, Iztok
AU - Iglesias, Andres
AU - Galvez, Akemi
AU - Del Ser, Javier
AU - Osaba, Eneko
AU - Fister, Iztok
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - Association rule mining is a method for identification of dependence rules between features in a transaction database. In the past years, researchers applied the method using features consisting of categorical attributes. Rarely, numerical attributes were used in these studies. In this paper, we present a novel approach for mining association based on differential evolution, where features consist of numerical as well as categorical attributes. Thus, the problem is presented as a single objective optimization problem, where support and confidence of association rules are combined into a fitness function in order to determine the quality of the mined association rules. Initial experiments on sport data show that the proposed solution is promising for future development. Further challenges and problems are also exposed in this paper.
AB - Association rule mining is a method for identification of dependence rules between features in a transaction database. In the past years, researchers applied the method using features consisting of categorical attributes. Rarely, numerical attributes were used in these studies. In this paper, we present a novel approach for mining association based on differential evolution, where features consist of numerical as well as categorical attributes. Thus, the problem is presented as a single objective optimization problem, where support and confidence of association rules are combined into a fitness function in order to determine the quality of the mined association rules. Initial experiments on sport data show that the proposed solution is promising for future development. Further challenges and problems are also exposed in this paper.
KW - Association rule mining
KW - Classification
KW - Differential evolution
KW - Evolutionary computation
UR - https://www.scopus.com/pages/publications/85057076753
U2 - 10.1007/978-3-030-03493-1_9
DO - 10.1007/978-3-030-03493-1_9
M3 - Conference contribution
AN - SCOPUS:85057076753
SN - 9783030034924
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 79
EP - 88
BT - Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings
A2 - Yin, Hujun
A2 - Novais, Paulo
A2 - Camacho, David
A2 - Tallón-Ballesteros, Antonio J.
PB - Springer Verlag
T2 - 19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018
Y2 - 21 November 2018 through 23 November 2018
ER -