MMAC: A new multi-class, multi-label associative classification approach

Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Volumn , Issue , 2004, Pages 217-224

  Thabtah, Fadi A ^a   Cowling, Peter ^a   Peng, Yonghong ^b  

^a Modelling Optimisation Scheduling and Intelligent Computing Research Centre   (United Kingdom)

^b UNIVERSITY OF BRADFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COVERING ALGORITHMS; DECISION TREES; MULTI LABEL ASSOCIATIVE CLASSIFICATION (MMAC); MULTI LABEL CLASSIFICATION;

ALGORITHMS; DATABASE SYSTEMS; INFORMATION RETRIEVAL; KNOWLEDGE ENGINEERING; LEARNING SYSTEMS; PROBLEM SOLVING;

DATA MINING;

EID: 19544382863     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2004.10117     Document Type: Conference Paper

References (21)

1. R. Agrawal, T. Amielinski and A. Swami. Mining association rule between sets of items in large databases. In Proceeding of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, DC, May 26-28 1993, pp. 207-216.
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rule. In Proceeding of the 20th International Conference on Very Large Data Bases, 1994, pp. 487-499.
3. M. Boutell, X. Shen, J. Luo and C. Brown. Multi-label semantic scene classification. Technical report 813, Department of Computer Science, University of Rochester, Rochester, NY 14627 & Electronic Imaging Products R & D, Eastern Kodak Company, September 2003.
4. A. Clare and R.D. King. Knowledge discovery in multi-label phenotype data. In L. De Raedt and A. Siebes, editors, PKDD01, volume 2168 of Lecture Notes in Artificial Intelligence, Springer - Verlag, 2001, pp. 42-53.
5. P. Cowling and K. Chakhlevitch. Hyperheuristics for Managing a Large Collection of Low Level Heuristics to Schedule Personnel. In Proceeding of 2003 IEEE conference on Evolutionary Computation, Canberra, Australia, 8-12 Dec 2003.
6. R. Duda, P. Hart, and D. Strok. Pattern classification. Wiley, 2001.
7. E. Frank and I. Witten. Generating accurate rule sets without global optimisation. In Shavlik, J., ed., Machine Learning: In Proceedings of the Fifteenth International Conference, Madison, Wisconsin. Morgan Kaufmann Publishers, San Francisco, CA, pp. 144-151.
8. J. Furnkranz. Separate-and-conquer rule learning. Technical Report TR-96-25, Austrian Research Institute for Artificial Intelligence, Vienna, 1996.
9. W. Li, J. Han and J. Pei. CMAR: Accurate and efficient classification based on multiple class association rule. In ICDM'01, San Jose, CA, Nov. 2001, pp. 369-376.
10. T. Joachims. Text categorisation with Support Vector Machines: Learning with many relevant features. In Proceeding Tenth European Conference on Machine Learning, 1998, pp. 137-142.
11. T. S. Lim, W. Y. Loh and Y. S. Shih. A comparison of prediction accuracy, complexity and training time of thirty-three old and new classification algorithms. Machine Learning, 39, 2000.
12. B. Liu, W. Hsu and Y. Ma. Integrating Classification and association rule mining. In KDD '98, New York, NY, Aug. 1998.
13. J.R. Quinlan. C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, San Francisco, 1993.
14. th International Joint Conferences on Artificial Intelligence, Morgan Kaufmann, San Francisco, 1987, pp. 304-307.
15. R. Schapire and Y. Singer, "BoosTexter: A boosting-based system for text categorization," Machine Learning, vol. 39, no. 2/3, 2000, pp. 135-168.
16. F. Thabtah, P. Cowling and Y. Peng. Comparison of Classification techniques for a personnel scheduling problem. In Proceeding of the 2004 International Business Information Management Conference, Amman, July 2004.
17. Y. Yang. An evaluation of statistical approaches to text categorisation. Technical Report CMU-CS-97-127, Carnegie Mellon University, April 1997.
18. X. Yin and J. Han. CPAR: Classification based on predictive association rule. In SDM 2003, San Francisco, CA, May 2003.
19. CBA:http://www.comp.nus.edu.sg/~dm2/p_download.html
20. Weka: Data Mining Software in Java: http://www.cs.waikato.ac.nz/ml/weka.
21. M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. New algorithms for fast discovery of association rules. In Proceedings of the 3rd KDD Conference, Aug. 1997, pp.283-286.