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SET-PSO-based approach for mining positive and negative association rules

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Abstract

Data mining is the process of determining new, unanticipated, valuable patterns from existing databases by considering historical and recent developments in statistics, artificial intelligence, and machine learning. It can help companies focus on the most important information in their data warehouses. Association rule mining is one of the most highly researched and popular data mining techniques for finding associations between items in a set. It is frequently used in marketing, advertising, and inventory control. Typically, association rules only consider items in transactions (positive association rules). They do not consider items that do not occur together, which can be used to create rules that are also useful for market basket analysis. Also, existing algorithms often generate too many candidate itemsets when mining the data and scan the database multiple times. To resolve these issues in association rule mining algorithms, we propose SARIC (set particle swarm optimization for association rules using the itemset range and correlation coefficient). Our method uses set particle swarm optimization to generate association rules from a database and considers both positive and negative occurrences of attributes. SARIC applies the itemset range and correlation coefficient so that we do not need to specify the minimum support and confidence, because it automatically determines them quickly and objectively. We verified the efficiency of SARIC using two differently sized databases. Our simulation results demonstrate that SARIC generates more promising results than Apriori, Eclat, HMINE, and a genetic algorithm.

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References

  1. Chen MS, Han J, Sal YuP (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8:866–883

    Article  Google Scholar 

  2. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD international conference management of date. ACM, Washington DC, pp 207–216

  3. Han J, Kamber M (2006) Data mining: concepts and techniques, 2nd edn. Morgan Kaufmann Publishers, ISBN 1-55860-901-6

  4. Brin S, Motwani R, Silverstein C (1997) Beyond market: generalizing association rules to correlations. In: Proceedings of the ACM SIGMOD conference, vol 26. ACM Press, New York, pp 265–276

  5. Luo J, Bo Z (2010) Research on mining positive and negative association rules. In: Proceedings of the computer and communication technologies in agriculture engineering (CCTAE). IEEE, vol 3, pp 302–304

  6. Wu X, Zhang C, Zhang S (2004) Efficient mining of both positive and negative association rules. ACM Trans Inf Syst 22(3):381–405

    Article  Google Scholar 

  7. Kumar KS, Chezian RM (2012) A survey on association rule mining using apriori algorithm. Int J Comput Appl 45:47–50

    Google Scholar 

  8. Zhang M, He C (2010) Survey on association rule mining algorithms. Adv Comput Commun Control Manag 56:111–118

    Article  Google Scholar 

  9. Zaki MJ, Parthasarathy S, Ogihara M, Li W (1997) New algorithms for fast discovery of association rules. In: Proceedings of the international conference on knowledge discovery and data mining (KDD), pp 283–286

  10. Wur S-Y, Leu Y (1999) An effective Boolean algorithm for mining association rules in large databases. In: Proceedings of the international conference on database systems for advanced applications. IEEE

  11. Pei J, Han J, Lu H, Nishio S, Tang S, Yang D (2001) H-Mine: hyper structure mining of frequent patterns in large databases. In: Proceedings of the international conference on data mining. IEEE, pp 441–448

  12. Hamrouni T, Ben Yahia S, Mephu Nguifo E (2009) Sweeping the disjunctive search space towards mining new exact concise representations of frequent itemsets. Data Knowl Eng 68:1091–1111

    Article  Google Scholar 

  13. Fournier-Viger P, Faghihi U, Nkambou R, Nguifo EM (2011) CMRules: mining sequential rules common to several sequences. Knowl Based Syst 25:63–76

    Article  Google Scholar 

  14. Nikambou R, Fournier-Viger P, Nguifo EM (2011) Learning task models in ill domain using an hybrid knowledge discovery framework. Knowl Based Syst 24:176–185

    Article  Google Scholar 

  15. Faghihi U, Fournier-Viger P, Nkambou R (2012) A computational model for causal learning in cognitive agents. Knowl Based Syst 30:48–56

    Article  Google Scholar 

  16. Jain JK, Tiwari N, Ramaiya M (2013) A survey: on association rule mining. Int J Eng Res Appl (IJERA) 3:2065–2069

    Google Scholar 

  17. Kumar NVSP, Rao LJJ, Kumar GV (2012) A study on positive and negative association rule mining. Int J Eng Rese Technol 1:1–5

  18. Suryawansi N, Jain S, Jain A (2012) A review of negative and positive association rule mining with multiple constraint and correlation factor. Int J Emerg Technol Adv Eng (IJETAE) 2:778–781

    Google Scholar 

  19. Swesi IMAO, Bakar AA, Kadir ASA (2012) Mining positive and negative association rules from interesting frequent and infrequent itemsets. In: 9th international conference on fuzzy systems and knowledge discovery (FSKD), IEEE pp 650–655

  20. Fournier-Viger P, Wu C-W, Tseng VS (2012) Mining top-k association rules. In: Proceedings of the 25th Canadian conference on artificial intelligence, LNAI 7310. Springer, Berlin, pp 61–73

  21. Kamsu-Foguem B, Rigal F, Mauget F (2013) Mining association rules for the quality improvement of the production process. Expert Syst Appl 40:1034–1045

    Article  Google Scholar 

  22. Sharma A, Tivari N (2012) A survey of association rule mining using genetic algorithm. Int J Comput Appl Inf Technol 1:5–11

    Google Scholar 

  23. Abdi MJ, Dr Giveki (2012) Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules. Eng Appl Artif Intell 26:603–608

    Article  Google Scholar 

  24. Kuo RJ, Wang MJ, Huang TW (2011) Application of particle swarm optimization to association rule mining. Appl Soft Comput 11:326–336

    Article  Google Scholar 

  25. Ankita S, Shikha A, Jitendra A, Sanjeev S (2013) A review on application of particle swarm optimization in association rule mining. In: Proceedings of the international conference on Frontiers in intelligent computing. Springer advances in intelligent systems and computing, vol 199. Springer, Berlin, pp 405–414

  26. Cai G-R, Li S-Z, Chen S-L (2010) Mining fuzzy association rules by using non linear particle swarm optimization. Adv Intell Soft Comput 82:621–630

    Article  Google Scholar 

  27. Mishra S, Mishra D, Satapathy SK (2012) CLPSO-fuzzy frequent pattern mining from gene expression data. In: Proceedings of the international conference on computer, communication, control and information technology (C3IT), vol 4, pp 807–811

  28. Nandhini M, Janani M, Sivanandham SN (2012) Association rule mining using swarm intelligence and domain ontology. In: Proceedings of the international conference on recent trends in information technology. IEEE, pp 537–541

  29. Chauhan A, Bansal A (2012) Negative association rule mining through particle swarm optimization. In: Proceedings of the international conference in recent trends in information technology and computer science. International Journal of Computer Applications (IJCA), pp 18–22

  30. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: IEEE 4th international conference on neural networks, vol 4, pp 1942–1948

  31. Abraham A, Guo H, Liu H (2006) Swarm intelligence: foundations, perspectives and applications. Stud Comput Intell 26:3–25

    Article  Google Scholar 

  32. Piao X, Wang Z, Liu G (2010) Research on mining positive and negative association rules based on dual confidence. In: Proceedings of the fifth international conference on internet computing for science and engineering. IEEE, pp 102–105

  33. Neethling M, Engelbrecht AP (2006) Determining RNA secondary structure using set-based particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation, IEEE

  34. Track Open Source Project (2003) Extended bakery dataset. Integrated SCM & Project Management, https://wiki.csc.calpoly.edu/datasets/wiki/ExtendedBakery20k

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers for their detailed, valuable comments and constructive suggestions.

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Authors and Affiliations

  1. School of Information Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India

    Jitendra Agrawal, Ankita Singhai & Sanjeev Sharma

  2. Department of Computer Science, UIT, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, India

    Shikha Agrawal

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  1. Jitendra Agrawal
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  2. Shikha Agrawal
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  4. Sanjeev Sharma
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Correspondence to Jitendra Agrawal.

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Agrawal, J., Agrawal, S., Singhai, A. et al. SET-PSO-based approach for mining positive and negative association rules. Knowl Inf Syst 45, 453–471 (2015). https://doi.org/10.1007/s10115-014-0795-2

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