A comparison between rule based and association rule mining algorithms

NSS 2009 - Network and System Security

Volumn , Issue , 2009, Pages 452-455

  Mazid, Mohammed M ^a   Ali, A B M Shawkat ^a   Tickle, Kevin S ^a  

^a CENTRAL QUEENSLAND UNIVERSITY   (Australia)

Author keywords

Apriori; Association rule mining; Classification; DARPA (Defense Advanced Research Projects Agency); Partial Decision Tree (PART)

Indexed keywords

APRIORI; ASSOCIATION RULE MINING; CLASSIFICATION; CLASSIFICATION ALGORITHM; DATA MINING PROBLEMS; DEFENSE ADVANCED RESEARCH PROJECTS AGENCIES; MINING TASKS; OPTIMUM ALGORITHM; RULE BASED; RULE-BASED CLASSIFICATION; TEST SETS;

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; COMPUTATIONAL COMPLEXITY; DECISION TREES; INTRUSION DETECTION; LEARNING ALGORITHMS; MINING; NETWORK SECURITY; RESEARCH;

DATA MINING;

EID: 72849117836     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/NSS.2009.81     Document Type: Conference Paper

References (19)

1. R. Agrawal, T. Imielinski, and A. Swami, "Mining associations between sets of items in large databases," In Proc. of the ACM SIGMOD Int'l Conference on Management of Data. - Washington D.C., 1993, pp. 207-216,
2. T. Scheffer, "Finding association rules that trade support optimally against confidence," In proceedings of the 5th European Conference on Principles and Practice of Knowlege Discovery in Databases(PKDD'01). - Freiburg, Germany: Springer-Verlag, 2001, pp. 424-435.
3. P.A. Flach and N. Lachiche, "Confirmation-guided discovery of first-order rules with Tertius," Kluwer Academic Publishers. - The Netherlands, 2001, Vol. 42. - pp. 61-95.
4. J. Pei, J. Han, and R. Mao, "Closet: An efficient algorithm for mining frequent closed itemsets", In Proc. SIGMOD Int'l Workshop Data Mining and Knowledge Discovery. 2000.
5. D. Burdick, Calimlim M. and J. Gehrke, "MAFIA: a maximal frequent itemset algorithm for transactional databases," In Intlernational conference on Data Engineering, 2001.
6. M. J. Zaki, "Scalable algorithms for association mining," IEEE Transaction on Knowledge and Data Engineering, 2000. vol. 12.
7. M. J. Zaki, and C.J. Hsiao, "CHARM: An efficient algorithm for closed association rule mining," Computer Science Department, Rensselaer Polytechnic Institute, New York, 1999.
8. J. R. Quinlan, "C4.5: Program for machine learning," Morgan Kaufmann. 1992.
9. P. Clark, and T. Niblitt, "The CN2 induction algorithm," Machine Learning, 1989, vol. 3, issue 1.
10. W. Cohen, "Fast effective rule induction," In Proceedings of ICML-95, San Francisco, CA. 1995.
11. R. Duda and P. Hart, "Pattern classification and scene analysis," Wiley, New York. 1973.
12. Lincoln Labrotary. DARPA-99 Intrusion Detection Evaluation Data Set. Massachusetts Institute of technology, Retrieved January 12, 2009 from http://www.ll.mit.edu/mission/communications/ist/corpora/ideval/data/1999data. html
13. I.H. Witten, and E. Frank, "Data mining: practical machine learning tool and technique with java implementation," Morgan Kaufmann, San Francisco, 2000.
14. E. Frank and I.H. Witten, "Generating accurate rule sets without global optimisation," in Proceedings of the Fifteenth International Conference, Morgan Kaufmann, San Francisco, CA, 1998.
15. G. Pagallo and D. Haussler, "Boolean feature discovery in empirical learning," Machine Learning, 1990, vol. 5(1), pp. 71-99.
16. F.J emili, M. Zaghdoud and M. B. Ahmed, "A framework for an adaptive intrusion detection system using bayesian network," IEEEXplore, 2007. pp 66.
17. A.B.M. S. Ali, and K. A. Smith, "On learning algorithm for classification," Applied Soft Computing, 2004, pp. 119-138.
18. A.B.M. S. Ali, and S. A. Wasimi, "Data mining: methods and techniques," Thomson Publishers, Victoria, Australia, 2007.
19. A.B.M. S. Ali, "Automated support vector learning algorithms," PhD Thesis, Monash University, Victoria, Australia. 2005.