Irrelevant Feature and Rule Removal for Structural Associative Classification Using Structure-Preserving Flat Representation - Feature Selection for Data and Pattern Recognition - page 220

8. Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: generalizing association rules
to correlations. In: Proceedings of ACM SIGMOD International Conference on Management
of Data, pp. 265-276 (1997)
9. Cheng, H., Yan, X., Han, J., Hsu, C.W.: Discriminative frequent pattern analysis for effective
classification. In: Proceedings of the 23rd International IEEE Conference on Data Engineering,
pp. 716-725 (2007)
10. Cheng, H., Yan, X., Han, J., Yu, P.: Direct discriminative pattern mining for effective classifi-
cation. In: Proceedings of the 24th International Conference on Data Engineering, pp. 167-178
(2008)
11. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1 (3), 131-156 (1997)
12. Geng, L., Hamilton, H.: Interestingness measures for data mining: a survey. ACM Comput.
Surv. 338 (3, Article No. 9) (2006)
13. Goodman, A., Kamath, C., Kumar, V.: Data analysis in the 21st century. Stat. Anal. Data Min.
1 (1), 1-3 (2008)
14. Hadzic, F.: A structure preserving flat data format representation for tree-structured data. In:
Proceedings of PAKDD Workshops, vol. 2011, pp. 221-233 (2012)
15. Hadzic, F., Dillon, T.: Using the symmetrical tau (

˄

) criterion for feature selection in decision
tree and neural network learning. In: Proceedings of the 2nd SIAM Workshop on Feature
Selection for Data Mining: Interfacing Machine Learning and Statistics (2006)
16. Hadzic, F., Hecker, M.: Alternative approach to tree-structured web log representation and
mining. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence
and Intelligent Agent Technology, pp. 235-242 (2011)
17. Hadzic, F., Tan, H., Dillon, T.S.: Mining of Data With Complex Structures, 1st edn, Studies in
Computational Intelligence, vol. 333, . Springer (2011)
18. Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann (2001)
19. Hashimoto, K., Takigawa, I., Shiga, M., Kanehisa, M., Mamitsuka, H.: Mining significant tree
patterns in carbohydrate sugar chains. Bioinformatics 24 (16), 167-173 (2008)
20. Knijf, J.D., Feelders, A.J.: Monotone constraints in frequent tree mining. In: Proceedings of the
14th Annual Machine Learning Conference of Belgium and the Netherlands, BENELEARN
pp. 13-20 (2005)
21. Kudo, M., Sklansky, J.: Comparison of algorithms that select features for pattern classifiers.
Pattern Recognit. 33 (1), 25-41 (2000)
22. Lallich, S., Teytaud, O., Prudhomme, E.: Association rule interestingness: measure and statis-
tical validation. In: Quality Measures in Data Mining. Studies in Computational Intelligence,
vol. 43, pp. 251-275. Springer (2007)
23. Lallich, S., Teytaud, O., Prudhomme, E.: Formal framework for the study of algorithmic prop-
erties of objective interestingness measures. In: Data Mining: Foundations and Intelligent
Paradigms, vol. 24, pp. 77-98. ISRL (2012)
24. Le Bras, Y., Lenca, P., Lallich, S.: Mining classification rules without support: an anti-monotone
property of Jaccardmeasure. In: Proceedings of the 14th International Conference onDiscovery
Science, pp. 179-193 (2011)
25. Lenca, P., Meyer, P., Vaillant, B., Lallich, S.: On selecting interestingness measures for asso-
ciation rules: user oriented description and multiple criteria decision aid. Eur. J. Oper. Res.
184 (2), 610-626 (2008)
26. Li, J., Shen, H., Topor, R.: Mining the optimal class association rule set. Knowl.-Based Syst.
15 (7), 399-405 (2002)
27. Little, R., Rubin, D.: Statistical Analysis with Missing Data, 2nd edn. Wiley, New York (2002)
28. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings
of the 4th International Conference on Knowledge Discovery and Data Mining, pp. 80-86
(1998)
29. McGarry, K.: A survey of interestingness measures for knowledge discovery. Knowl. Eng. Rev.
20 (1), 39-61 (2005)
30. Molina, L., Belanche, L., Nebot, A.: Feature selection algorithms: a survey and experimental
evaluation. In: Proceedings of IEEE International Conference on Data Mining, pp. 306-313
(2002)