Mining frequent patterns and association rules using similarities

Expert Systems with Applications

Volumn 40, Issue 17, 2013, Pages 6823-6836

  Rodríguez González, Ansel Y ^a   Martínez Trinidad, José Fco ^a   Carrasco Ochoa, Jesús A ^a   Ruiz Shulcloper, José ^b  

^a INSTITUTO NACIONAL DE ASTROFÍSICA   (Mexico)

^b Advanced Technologies Applications Center (CENATAV)   (Cuba)

Author keywords

Association rules; Data mining; Downward closure property; Frequent patterns; Mixed data; Similarity functions

Indexed keywords

DOWNWARD CLOSURE PROPERTIES; FREQUENT PATTERN MINING; FREQUENT PATTERNS; MINING ASSOCIATIONS; MIXED DATA; QUALITY PATTERNS; REAL-WORLD PROBLEM; SIMILARITY FUNCTIONS;

ALGORITHMS; ASSOCIATION RULES;

DATA MINING;

EID: 84880519846     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.06.041     Document Type: Article

References (35)

1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. Research Report RJ 9839, IBM Almaden Research Center, San Jose, California.
2. Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In 1993 ACM SIGMOD international conference on management of data (pp. 207-216). Washington, USA.
3. R. Agrawal, and R. Srikant Fast algorithms for mining association rules 20th International Conference on Very Large Data Bases, Santiago de Chile, Chile 1994 Morgan Kaufmann 487 499
4. B. Alatas, E. Akin, and A. Karci Modenar: multi-objective differential evolution algorithm for mining numeric association rules Applied Soft Computing 8 2008 646 656 (Pubitemid 47374595)
5. S. Ayubi, M.K. Muyeba, A. Baraani, and J. Keane An algorithm to mine general association rules from tabular data Information Sciences 179 2009 3520 3539
6. R. Chaves, J. Ramrez, and J.M. Grriz Association rule-based feature selection method for Alzheimers disease diagnosis Expert Systems with Applications 39 2012 11766 11774
7. R. Chaves, J. Ramrez, and J.M. Grriz Integrating discretization and association rule-based classification for Alzheimers disease diagnosis Expert Systems with Applications 40 2013 1571 1578
8. G. Chen, and Q. Wei Fuzzy association rules and the extended mining algorithms Information Sciences 147 2000 201 228
9. Choi, D.W., & Hyun, Y.J., (2010). Transitive association rule discovery by considering strategic importance. In 2010 10th IEEE international conference on computer and information technology (pp. 1654-1659). Bradford UK.
10. Dánger, R., Ruiz-Shulcloper, J., & Berlanga, R. (2004). Objectminer: a new approach for mining complex objects. In ICEIS-2004 (pp. 42-47). Oporto, Portugal.
11. S. Dua, H. Singh, and H.W. Thompson Associative classification of mammograms using weighted rules Expert Systems with Applications 36 2009 9250 9259
12. L. Ertoz, E. Eilertson, A. Lazarevic, P.N. Tan, V. Kumar, and J. Srivastava The MINDS: Minnesota Intrusion Detection System Next Generation Data Mining 2004 MIT Press
13. J. Gómez, O. Rodríguez, S. Valladares, and J. Ruiz-Shulcloper Applying Mathematical Modeling Geophysical International 33 1994 447 467
14. Raudel Hernández-León, Jesús A. Carrasco-Ochoa, José Fco. Martínez-Trinidad, and José Hernández- Palancar Classification based on specific rules and inexact coverage Expert Systems with Applications 39 2012 11203 11211
15. T. Hu, S.Y. Sung, H. Xiong, and Q. Fu Discovery of maximum length frequent itemsets Information Sciences 178 2008 69 87 (Pubitemid 47531788)
16. B. Kalpana, and R. Nadarajan Incorporating heuristics for efficient search space pruning in frequent itemset mining strategies Current Science 94 2008 97 101 (Pubitemid 351184440)
17. C. LaRosa, L. Xiong, and K. Mandelberg Frequent pattern mining for kernel trace data 2008 ACM Symposium on Applied Computing 2008 ACM Fortaleza, Ceara, Brazil 880 885
18. K. Leong, S. Chan, V. Ng, and S. Shiu Introduction of STEM: Space-Time-Event Model for crime pattern analysis Asian Journal of Information Technology 7 2008 516 523
19. X. Li, and Z. Deng Mining frequent patterns from network flows for monitoring network Expert Systems with Applications 37 2010 8850 8860
20. Li-Min, T., Shu-Jing, L., & Don-Lin, Y. (2010). Efficient mining of generalized negative association rules. In 2010 IEEE international conference on granular computing. (pp. 471-476). Silicon Valley, USA.
21. F.J. Lopez, A. Blanco, F. Garcia, C. Cano, and A. Marin Fuzzy association rules for biological data analysis: a case study on yeast BMC Bioinformatics 9 2008 107
22. H.H. Malik, J.R. Kender, D. Fradkin, and F. Moerchen Hierarchical document clustering using local patterns Data Mining and Knowledge Discovery 21 2010 153 185
23. J.F. Martínez-Trinidad, J. Ruiz-Shulcloper, and M.S. Lazo-Cortés Structuralization of universes Fuzzy Sets and Systems 112 2000 485 500
24. J. Nahar, T. Imam, K.S. Tickle, and Y.P. Chen Association rule mining to detect factors which contribute to heart disease in males and females Expert Systems with Applications 40 2013 1086 1093
25. Loan T.T. Nguyen, Bay Vo, Tzung-Pei Hong, and Hoang Chi Thanh Classification based on association rules: a lattice-based approach Expert Systems with Applications 39 2012 11357 11366
26. M.R. Ortiz-Posadas, L. Vega-Alvarado, and B. Toni A mathematical function to evaluate surgical complexity of cleft lip and palate Computer Methods and Programs Biomedicine 94 2009 232 238
27. Patil, B.M., Joshi, R.C., & Toshniwal, D. (2010). Association rule for classification of type-2 diabetic patients. In 2010 second international conference on machine learning and computing. (pp. 330-334). Bangalore, India.
28. A.Y. Rodríguez-González, J.F. Martínez-Trinidad, J.A. Carrasco-Ochoa, and J. Ruiz-Shulcloper Mining frequent similar patterns on mixed data J. Ruiz-Shulcloper, W. Kropatsch, Progress in Pattern Recognition Image Analysis and Applications, LNCS vol. 5197 2008 Springer-Verlag Berlin, Germany 136 144
29. A.Y. Rodríguez-González, J.F. Martínez-Trinidad, J.A. Carrasco-Ochoa, and J. Ruiz-Shulcloper RP-Miner: a relaxed prune algorithm for frequent similar pattern mining Knowledge and Information Systems 27 2011 451 471
30. J. Ruiz-Shulcloper, and A. Fuentes-Rodríguez A cybernetic model to analyze juvenile delinquency Revista Ciencias Matemticas 2 1981 123 153
31. D. Sánchez, M.A. Vila, L. Cerda, and J.M. Serrano Association rules applied to credit card fraud detection Expert Systems with Applications 36 2009 3630 3640
32. K.W. Wong, S. Zhou, Q. Yang, and J.M. Yeung Mining customer value: from association rules to direct marketing Data Mining and Knowledge Discovery 11 2005 57 79 (Pubitemid 41202923)
33. L. Xin, and D. Zhi-Hong Mining frequent patterns from network flows for monitoring network Expert Systems with Applications 37 12 2010
34. H. Ya-Han, and C. Yen-Liang Mining association rules with multiple minimum supports: a new mining algorithm and a support tuning mechanism Decision Support Systems 42 2006 1 24
35. Yunyan, L., & Juan, C. (2010). Application of association rules mining in marketing decision-making based on rough set. In 2010 International conference on e-business and e-government (pp. 3749-3752). Guangzhou, China.