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FP-Tree and Its Variants: Towards Solving the Pattern Mining Challenges

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Proceedings of First International Conference on Smart System, Innovations and Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 79))

Abstract

Mining patterns from databases is like searching for precious gems which is a gruesome task but still a rewarding one. The frequent patterns are believed to be valuable assets for the researchers that provide them useful information. The frequent and rare pattern mining paradigm is broadly divided into Apriori and FP-Tree-based approaches. Experimental results and performance evaluation available in the literature have established the fact that FP-Tree-based approaches are superior to the Apriori ones on various grounds. This paper explores the various modifications of FP-Tree that were developed to tackle the major pattern mining research challenges. Through this paper, an attempt has been made to review the usefulness and applicability of the most eminent data structure in the domain of pattern mining, the FP-Tree.

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References

  1. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Acm sigmod record. vol. 22, pp. 207–216. ACM (1993).

    Google Scholar 

  2. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM Sigmod Record. vol. 29, pp. 1–12. ACM (2000).

    Google Scholar 

  3. Liu, G., Lu, H., Yu, J.X., Wang, W., Xiao, X.: Afopt: An efficient implementation of pattern growth approach. In: FIMI (2003).

    Google Scholar 

  4. Racz, B.: nonordfp: An fp-growth variation without rebuilding the fp-tree. In: FIMI (2004).

    Google Scholar 

  5. Wang, K., Tang, L., Han, J., Liu, J.: Top down fp-growth for association rule mining. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. pp. 334–340. Springer (2002).

    Google Scholar 

  6. Sucahyo, Y.G., Gopalan, R.P.: Ct-pro: A bottom-up non recursive frequent Itemset mining algorithm using compressed fp-tree data structure. In: FIMI. vol. 4, pp. 212–223 (2004).

    Google Scholar 

  7. Grahne, G., Zhu, J.: Efficiently using prefix-trees in mining frequent itemsets. In: FIMI. vol. 90 (2003).

    Google Scholar 

  8. Pei, J., Han, J., Mao, R., et al.: Closet: An efficient algorithm for mining frequent closed itemsets. In: ACM SIGMOD workshop on research issues in data mining and knowledge discovery. vol. 4, pp. 21–30 (2000).

    Google Scholar 

  9. Wang, J., Han, J., Pei, J.: Closet+: Searching for the best strategies for mining frequent closed itemsets. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 236–245. ACM (2003).

    Google Scholar 

  10. Grahne, G., Zhu, J.: Fast algorithms for frequent itemset mining using fp-trees. IEEE transactions on knowledge and data engineering 17(10), 1347–1362 (2005).

    Google Scholar 

  11. Adnan, M., Alhajj, R.: Drfp-tree: disk-resident frequent pattern tree. Applied Intelligence 30(2), 84–97 (2009).

    Google Scholar 

  12. Bonchi, F., Goethals, B.: Fp-bonsai: the art of growing and pruning small fp-trees. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. pp. 155–160. Springer (2004).

    Google Scholar 

  13. Xu, B., Yi, T., Wu, F., Chen, Z.: An incremental updating algorithm for mining association rules. Journal of Electronics (China) 19(4), 403–407 (2002).

    Google Scholar 

  14. Koh, J.L., Shieh, S.F.: An efficient approach for maintaining association rules based on adjusting fp-tree structures. In: International Conference on Database Systems for Advanced Applications. pp. 417–424. Springer (2004).

    Google Scholar 

  15. Cheung, W., Zaiane, O.R.: Incremental mining of frequent patterns without candidate generation or support constraint. In: Database Engineering and Applications Symposium, 2003. Proceedings. Seventh International. pp. 111–116. IEEE (2003).

    Google Scholar 

  16. Leung, C.K.S., Khan, Q.I., Li, Z., Hoque, T.: Cantree: a canonical-order tree for incremental frequent-pattern mining. Knowledge and Information Systems 11(3), 287–311 (2007).

    Google Scholar 

  17. Tanbeer, S.K., Ahmed, C.F., Jeong, B.S., Lee, Y.K.: Cp-tree: a tree structure for single-pass frequent pattern mining. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. pp. 1022–1027. Springer (2008).

    Google Scholar 

  18. Leung, C.K.S., Carmichael, C.L., Hao, B.: Efficient mining of frequent patterns from uncertain data. In: Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007). pp. 489–494. IEEE (2007).

    Google Scholar 

  19. Calders, T., Garboni, C., Goethals, B.: Efficient pattern mining of uncertain data with sampling. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. pp. 480–487. Springer (2010).

    Google Scholar 

  20. Tseng, V.S., Wu, C.W., Shie, B.E., Yu, P.S.: Up-growth: an efficient algorithm for high utility itemset mining. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. pp. 253–262. ACM (2010).

    Google Scholar 

  21. Tseng, V.S., Shie, B.E., Wu, C.W., Philip, S.Y.: Efficient algorithms for mining high utility itemsets from transactional databases. IEEE transactions on knowledge and data engineering 25(8), 1772–1786 (2013).

    Google Scholar 

  22. Lin, C.W., Hong, T.P., Lu, W.H.: An effective tree structure for mining high utility itemsets. Expert Systems with Applications 38(6), 7419–7424 (2011).

    Google Scholar 

  23. Lin, C.W., Hong, T.P., Lu, W.H., Lin, W.Y.: An incremental fusp-tree maintenance algorithm. In: 2008 Eighth International Conference on Intelligent Systems Design and Applications. vol. 1, pp. 445–449. IEEE (2008).

    Google Scholar 

  24. Pei, J., Han, J., Mortazavi-Asl, B., Zhu, H.: Mining access patterns efficiently from web logs. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining. pp. 396–407. Springer (2000).

    Google Scholar 

  25. Grahne, G., Zhu, J.: High performance mining of maximal frequent itemsets. In:6th International Workshop on High Performance Data Mining (2003).

    Google Scholar 

  26. Yan, Y.J., Li, Z.J., Chen, H.W.: Efficiently mining of maximal frequent item sets based on fp-tree. Ruan Jian Xue Bao (J. Softw.) 16(2), 215–222 (2005).

    Google Scholar 

  27. Giannella, C., Han, J., Pei, J., Yan, X., Yu, P.S.: Mining frequent patterns in data streams at multiple time granularities. Next generation data mining 212, 191–212 (2003).

    Google Scholar 

  28. Leung, C.K.S., Khan, Q.I.: Dstree: a tree structure for the mining of frequent sets from data streams. In: Sixth International Conference on Data Mining (ICDM’06). pp. 928–932. IEEE (2006).

    Google Scholar 

  29. Tanbeer, S.K., Ahmed, C.F., Jeong, B.S., Lee, Y.K.: Efficient frequent pattern mining over data streams. In: Proceedings of the 17th ACM conference on Information and knowledge management. pp. 1447–1448. ACM (2008).

    Google Scholar 

  30. Hu, Y.H., Chen, Y.L.: Mining association rules with multiple minimum supports: anew mining algorithm and a support tuning mechanism. Decision Support Systems 42(1), 1–24 (2006).

    Google Scholar 

  31. Tsang, S., Koh, Y.S., Dobbie, G.: Rp-tree: rare pattern tree mining. In: Data Warehousing and Knowledge Discovery, pp. 277–288. Springer (2011).

    Google Scholar 

  32. Bhatt, U., Patel, P.: A novel approach for finding rare items based on multiple minimum support framework. Procedia Computer Science 57, 1088–1095 (2015).

    Google Scholar 

  33. Chen, M., Gao, X., Li, H.: An efficient parallel fp-growth algorithm. In: Cyber-Enabled Distributed Computing and Knowledge Discovery, 2009. CyberC’09. International Conference on. pp. 283–286. IEEE (2009).

    Google Scholar 

  34. Leung, C.K.S., Hayduk, Y.: Mining frequent patterns from uncertain data with map reduce for big data analytics. In: International Conference on Database Systems for Advanced Applications. pp. 440–455. Springer (2013).

    Google Scholar 

  35. Chang, H.Y., Lin, J.C., Cheng, M.L., Huang, S.C.: A novel incremental data mining algorithm based on fp-growth for big data. In: Networking and Network Applications (NaNA), 2016 International Conference on. pp. 375–378. IEEE (2016).

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Tezpur University, Napaam, Sonitpur, 784028, Assam, India

    Anindita Borah & Bhabesh Nath

Authors
  1. Anindita Borah
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  2. Bhabesh Nath
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Correspondence to Anindita Borah .

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Editors and Affiliations

  1. College of Engineering, Iowa State University, Ames, Iowa, USA

    Arun K. Somani

  2. Department of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India

    Sumit Srivastava

  3. Department of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India

    Ankit Mundra

  4. Department of Electronics and Communication Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

    Sanyog Rawat

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Borah, A., Nath, B. (2018). FP-Tree and Its Variants: Towards Solving the Pattern Mining Challenges. In: Somani, A., Srivastava, S., Mundra, A., Rawat, S. (eds) Proceedings of First International Conference on Smart System, Innovations and Computing. Smart Innovation, Systems and Technologies, vol 79. Springer, Singapore. https://doi.org/10.1007/978-981-10-5828-8_51

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  • DOI: https://doi.org/10.1007/978-981-10-5828-8_51

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5827-1

  • Online ISBN: 978-981-10-5828-8

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