Multiple labels associative classification

1. Agrawal, R., Imielinski, T., Swami, A.: Mining associations between sets of items in massive databases. In: Buneman, P., Jajodia, S. (eds) Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 207–216. Washington, DC (1993)

2. Agrawal, R., Srikant, R.: Fast algorithms for mining association rule. In: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487–499 (1995)

3. Ali, K., Manganaris, S., Srikant, R.: Partial classification using association rules. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pp. 115–118. AAAI (1997)

4. Antonie, M., Zaiane, O.: An associative classifier based on positive and negative rules. In: Proceedings of the 9th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 64–69. Paris, France (2004)

5. Boutell, M., Shen, X., Luo, J., Brown, C.: 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 (2003)

6. Clare, A., King, R.: Knowledge discovery in multi-label phenotype data. In: De Raedt, L., Siebes, A. (eds) Proceedings of the PKDD '01, vol. 2168, Lecture notes in Artificial Intelligence, pp. 42–53. Springer-Verlag, Berlin Heidelberg New York (2001)

7. Cohen, W.: Fast effective rule induction. In: Proceedings of the 12th International Conference on Machine Learning, pp. 115–123. Morgan Kaufmann, San Mateo (1995)

8. Cowling, P., Chakhlevitch, K.: Hyperheuristics for managing a large collection of low level heuristics to schedule personnel. In: Proceedings of the 2003 IEEE Conference on Evolutionary Computation, pp. 1214–1221. Canberra, Australia (2003)

9. Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley-Interscience, New York (2001)

10. Fayyad, U., Irani, K.: Multi-interval discritisation of continues-valued attributes for classification learning. In: Proceedings of the IJCAI, pp. 1022–1027 (1993)

11. Frank, E., Witten, I.: Generating accurate rule sets without global optimisation. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 144–151. Morgan Kaufmann Madison, Wisconsin, (1998)

12. Furnkranz, J.: Separate-and-conquer rule learning. Technical Report TR-96-25. Austrian Research Institute for Artificial Intelligence, Vienna (1996)

13. 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. Dallas, TX (2000)

14. Hipp, J., Guntzer, U., Nakaeizadeh, G.: Algorithms for association rule mining – a general survey and comparison. In: Proceedings of the ACM SIGKDD Explorations, pp. 58–64 (2000)

15. Joachims, T.: Text categorisation with support vector machines: learning with many relevant features. In: Proceedings of the Tenth European Conference on Machine Learning, pp. 137–142 (1998)

16. Li, W.: Classification based on multiple association rules. M.Sc. Thesis. Simon Fraser University (2001)

17. Li, W., Han, J., Pei, J.: CMAr: accurate and efficient classification based on multiple class association rule. In: Proceedings of the ICDM'01, pp. 369–376. San Jose, CA (2001)

18. Li, J., Shen, H., Topor, R.: Mining optimal class association rule set. In: Proceedings of the 5th Pacific–Asia Conference on Methodologies for Knowledge Discovery and Data Mining (PAKDD 2001), pp. 364–375 (2001)

19. Li, J., Topor, R., Shen, H.: Construct robust rule sets for classification. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 564–569. Edmonton, Alberta, Canada (2002)

20. Lim, T., Loh, W., Shih, Y.: A comparison of prediction accuracy, complexity and training time of thirty-three old and new classification algorithms. Machine Learn. 40, 203–228 (2000)

21. Liu, B., Hsu, W., Ma, Y.: Mining association rules with multiple minimum supports. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 337–341 (1999)

22. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the KDD '98, pp. 80–86. New York, NY (1998)

23. Merz, C.J., Murphy, P.M.: UCI repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine, CA (1996)

24. Park, J., Chen, M., Yu, P.: An effective Hash-based algorithm for mining association rules. In: Proceedings of the International Conference on Management of Data, pp. 175–186. San Jose, CA (1995)

25. Quinlan, J.: C4.5 Programs for Machine Learning. Morgan Kaufmann, San Mateo, San Francisco (1993)

26. Quinlan, J., Cameron-Jones, J.: FOIL: a midterm report. In: Proceedings of 1993 European Conference on Machine Learning, pp. 3–20. Vienna, Austria (1993)

27. Quinlan, J.: Generating production rules from decision trees. In: Proceedings of the 10th International Joint Conferences on Artificial Intelligence, pp. 304–307. Morgan Kaufmann, San Francisco (1987)

28. Schapire, R., Singer, Y.: BoosTexter: a boosting-based system for text categorization. Machine Learn. 39(2/3), 135–168 (2000)

29. Tan, P., Kumar, V.: Interestingness measures for association patterns: a prospective. In: Proceedings of the Workshop on Postprocessing in Machine Learning and Data Mining in Conjunction with Seventh ACM SIGKDD (2000)

30. Thabtah, F., Cowling, P., Peng, Y.: MCAR: multi-class classification based onassociation rule. In: Proceedings of the 3rd ACS/IEEE International Conference on Computer Systems and Applications, pp. 1–8. Cairo, Egypt (2005)

31. Thabtah, F., Cowling, P., Peng, Y.: Comparison of classification techniques for a personnel scheduling problem. In: Proceedings of the 2004 International Business Information Management Conference, pp. 207–213. Amman, Jordan (2004)

32. Witten, I., Frank, E.: Data mining: practical machine learning tools and techniques with java implementations. Morgan Kaufmann, San Francisco (2000)

33. Yang, Y.: An evaluation of statistical approaches to text categorisation. Technical Report CMU-CS-97-127. Carnegie Mellon University (1997)

34. Yin, X., Han, J.: CPAR: classification based on predictive association rule. In: Proceedings of the SDM 2003, pp. 369–376. San Francisco, CA (2003)

35. CBA (1998) CBA: http://www.comp.nus.edu.sg/dm2

36. Weka (2001) weka: data mining software in java. http://www.cs.waikato.ac.nz/ml/weka