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Performance Analysis of Tree-Based Approaches for Pattern Mining

  • Anindita BorahEmail author
  • Bhabesh Nath
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 711)

Abstract

Extracting meaningful patterns from databases has become a significant field of research for the data mining community. Researchers have skillfully taken up this task, contributing a range of frequent and rare pattern mining techniques. Literature subdivides the pattern mining techniques into two broad categories of level-wise and tree-based approaches. Studies illustrate that tree-based approaches outshine in terms of performance over the former ones at many instances. This paper aims to provide an empirical analysis of two well-known tree-based approaches in the field of frequent and rare pattern mining. Through this paper, an attempt has been made to let the researchers analyze the factors affecting the performance of the most widely accepted category of pattern mining techniques: the tree-based approaches.

Keywords

Frequent patterns Rare patterns Pattern mining Data structure 

References

  1. 1.
    Adnan, M., Alhajj, R.: Drfp-tree: disk-resident frequent pattern tree. Applied Intelligence 30(2), 84–97 (2009)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proc. 20th int. conf. very large data bases, VLDB. vol. 1215, pp. 487–499 (1994)Google Scholar
  3. 3.
    Bhatt, U., Patel, P.: A novel approach for finding rare items based on multiple minimum support framework. Procedia Computer Science 57, 1088–1095 (2015)CrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    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

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringTezpur UniversityNapaam, SonitpurIndia

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