Analysis of the effectiveness of G3PARM algorithm

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6077 LNAI, Issue PART 2, 2010, Pages 27-34

  Luna, J M ^a   Romero, J R ^a   Ventura, S ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

Author keywords

Association Rules; G3P; Genetic Programming

Indexed keywords

APRIORI; DATA SETS; DATA TYPE; EXPERIMENTAL STUDIES; FP-GROWTH ALGORITHM; G3P; GRAMMAR GUIDED GENETIC PROGRAMMING; MINING ASSOCIATION RULES; REAL-WORLD DATABASE;

ARTIFICIAL INTELLIGENCE; ASSOCIATION RULES; ASSOCIATIVE PROCESSING; GENETIC ALGORITHMS;

GENETIC PROGRAMMING;

EID: 77954569725     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13803-4_4     Document Type: Conference Paper

References (9)

1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB 1994, Proceedings of 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile, September 1994, pp. 487-499 (1994)
2. Borgelt, C.: Efficient implementations of Apriori and Eclat. In: FIMI 2003, 1st Workshop on Frequent Itemset Mining Implementations, Melbourne, Florida, USA (December 2003)
3. Coenen, F., Goulbourne, G., Leng, P.: Tree structures for mining association rules. Data Mining and Knowledge Discovery 8(1), 25-51 (2003)
4. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, New York (2003)
5. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 1-12 (2000)
6. Papè, N.F., Alcalá-Fdez, J., Bonarini, A., Herrera, F.: Evolutionary extraction of association rules: A preliminary study on their effectiveness. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS, vol. 5572, pp. 646-653. Springer, Heidelberg (2009)
7. Tsymbal, A., Pechenizkiy, M., Cunningham, P.: Sequential genetic search for ensemble feature selection. In: Nineteenth International Joint Conference on Artificial Intelligence (IJCAI 2005), Edinburgh, Scotland, August 2005, pp. 877-882 (2005)
8. Wei, Z., Hongzhi, L., Na, Z.: Research on the fp growth algorithm about association rule mining. In: ICFCC 2009, International Conference on Future Computer and Communication, Kuala Lumpur, Malaysia, April 2009, pp. 572-576 (2009)
9. Yang, G., Shimada, K., Mabu, S., Hirasawa, K.: A nonlinear model to rank association rules based on semantic similarity and genetic network programming, vol. 4, pp. 248-256. Institute of Electrical Engineers of Japan (2009)