A parallel genetic algorithm approach for automated discovery of censored production rules

Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, AIA 2007

Volumn , Issue , 2007, Pages 435-441

  Saroj, ^a   Bharadwaj, K K ^b  

^a GURU JAMBHESHWAR UNIVERSITY OF SCIENCE AND TECHNOLOGY   (India)

^b JAWAHARLAL NEHRU UNIVERSITY   (India)

Author keywords

Censored production rules; Data mining; Parallel genetic algorithm; Variable precision logic

Indexed keywords

CONSTRAINT THEORY; GENETIC ALGORITHMS; LOGIC PROGRAMMING; PARALLEL ALGORITHMS; PRECISION ENGINEERING;

CENSORED PRODUCTION RULES; PARALLEL GENETIC ALGORITHMS; VARIABLE PRECISION LOGIC;

DATA MINING;

EID: 38349137769     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. U.M. Fayyad, G.P. Shapiro, P. Smyth, The KDD process for extracting useful knowledge from volumes of data, Communication of ACM, 39(11), 1996, 27-34.
2. th International Workshop on Rough Sets, Fuzzy Sets and Machine Discovery, Tokyo, 1996, 225-232.
3. E. Suzuki, Autonomous discovery of reliable exceptions, Third International Conf. on Knowledge Discovery and Data Mining, Newport Beach, California, 1997, 259-262.
4. E. Suzuki, Undirected discovery of interesting exception rules, Int. Journal of Pattern Recognition Artificial Intelligence, 16(8), 2000, 1065-1086.
5. A. Silberschatz, A. Tuzhilin. What makes pattern interesting in knowledge discovery systems, IEEE Trans Knowledge Data Engg., 5(16), 1996, 970-974.
6. th International Conference on Knowledge Discovery and Data Mining, New York, 1998, 27-31.
7. B. Liu, W. Hsu, L-F. Mun, H.Y. Lee, Finding interesting patterns using user expectations, IEEE Tran Knowledge Data Engg., 11(6), 817-832.
8. E. Suzuki, Jan M. Zytkow, Unified algorithm for undirected discovery of exception rules, International Journal of Intelligent Systems, 20(1), 2005, 673-691.
9. A.A. Freitas, D.R. Carvalho, A genetic algorithm with sequential niching for discovering smalldisjunct rules, Appl. Soft Computation 2(2), 2002, 75-88.
10. R.S. Michalski, P. H. Winston, Variable precision logic, Artificial Intelligence, 29(2), 1986, 121-146.
11. K.K. Bharadwaj, Basheer M. Al-Maqaleh, Evolutionary approach for automated discovery of censored production rules, Enformatika 10, 2005, 147-152.
12. K.K. Bharadwaj, N.K. Jain, Hierarchical censored production rules (HCPRs) system, Data Mining and Knowledge Engg., 5(1), 1992, 19-34.
13. K.K. Bharadwaj, N. K. Jain, Some learning techniques in hierarchical censored production rules (HCPRs) system, Inter. Journal of Intelligent Systems, 13(4), 1997, 319-344.
14. K.K. Bharadwaj, N.K. Jain, N. M. Hawai, Adaptive hierarchical censored production rules system: a genetic algorithm approach, Advances in Artificial Intelligence SBIA '96', Lecture Notes in Artificial Intelligence, Curitiba, 1996, 81-90.
15. K. K. Bharadwaj, R. Varshneya, Parallelisation of hierarchical censored production rules, Information and Software Technology, 37(8), 1995, 453-460.
16. Rekha Kandwal, K. K. Bharadwaj, A Cumulative Learning Approach to Data Mining Employing Censored Production Rules (CPRs), Enformatika 7,. 2005 pp
17. A. Giordana, F. Neri, Search-intensive Concept induction, Evolutionary Computation 3(4), 1995, 375-416.
18. F. Neri, L. Saitta, Exploring the power of genetic search in learning symbolic classifiers, IEEE Transactions Pattern Recognition and machine Intel., 18(11), 1996, 1135-1141.
19. E. Noda, A.A.Freitas, A. H. Lopes, Discovering interesting prediction rules with a genetic algorithm, Proc. Conf. on Evolutionary Computation (CEC-99), Washington, D.C. 1999, 1322-1329.
20. I. W. Flockhart, N. J. Radcliffe, A genetic algorithm based approach to data mining, Proc. 2 Inter. Conf. Knowledge Discovery and Data Mining, Portland, 1996, 299-302.
21. E. Centu-Paz, A survey of parallel genetic algorithm, Calculateurs Paralleles, Reseaux et Systems, Repatis 10(2), 1998, 141-171.
22. W. Revera, Scalable parallel genetic algorithms, Artificial Intelligence Review, 2001, 153-168.
23. A. Enrique and M. Tomassini, Parallel and evolutionary algorithms, IEEE Transactions on Evolutionary Computation, 6(5), 2002, 443-462.
24. A.A. Freitas, A survey of parallel data mining, Advances in Evolutionary Computation Theory and Applications, Springer-Verlag, New York, 2003, 819-845
25. K. Wojciech, A parallel evolutionary algorithm for discovery of decision rules, Parallel Processing and Applied Mathematics, 5th International Conference, PPAM 2003, Lecture Notes in Computer Science 3019, 2004.
26. D. L. A Araujo., H. S. Lopes, A. A. Freitas, A parallel genetic algorithm for rule discovery in large databases, Proc. IEEE Systems, Man and Cybernetics Conference, Vol.III, Tokyo, 1999, 940-945.
27. th IntConf. Genetic Algorithms, 1997, 434-441.
28. X. Llora, J.M. Garrell, Knowledge-independent Data mining with fine grained parallel evolutionary algorithms, Proc. of GECCO, 2001, 461-468.
29. Department of Information and Computer Science, University of California, UCI Repository of Machine Learning databases, http://www.ics.uci.edu/ ~mlearn/MLRepositry.ht ml, 1994.