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Adaptive Cluster Based Discovery of High Utility Itemsets

  • Piyush LakhawatEmail author
  • Arun Somani
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 976)

Abstract

Utility Itemset Mining (UIM) is a key analysis technique for data which is modeled by the Transactional data model. While improving the computational time and space efficiency of the mining of itemsets is important, it is also critically important to predict future itemsets accurately. In today’s time, when both scientific and business competitive edge is commonly derived from first access to knowledge via advanced predictive ability, this problem becomes increasingly relevant. We established in our most recent work that having prior knowledge of approximate cluster structure of the dataset and using it implicitly in the mining process, can lend itself to accurate prediction of future itemsets. We evaluate the individual strength of each transaction while focusing on itemset prediction, and reshape the transaction utilities based on that. We extend our work by identifying that such reshaping of transaction utilities should be adaptive to the anticipated cluster structure, if there is a specific intended prediction window. We define novel concepts for making such an anticipation and integrate Time Series Forecasting into the evaluation. We perform additional illustrative experiments to demonstrate the application of our improved technique and also discuss future direction for this work.

Keywords

High utility itemset mining Clustering Adaptive itemset prediction Time Series forecasting 

Notes

Acknowledgements

The research reported in this paper is funded in part by Philip and Virginia Sproul Professorship Endowment and Jerry R. Junkins Endowments at Iowa State University. The research computation is supported by the HPC@ISU equipment at Iowa State University, some of which has been purchased through funding provided by NSF under MRI grant number CNS 1229081 and CRI grant number 1205413. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies. We also thank Dr. Mayank Mishra, former Post Doctoral Fellow at Iowa State University for his contributions, suggestions and feedback during the intial phase of this work. He was also co-author on the conference paper [14] of which this work is an extension.

References

  1. 1.
    Agrawal, R., Shafer, J.C.: Parallel mining of association rules. IEEE Trans. Knowl. Data Eng. 6, 962–969 (1996)CrossRefGoogle Scholar
  2. 2.
    Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB 1994, vol. 1215, pp. 487–499 (1994)Google Scholar
  3. 3.
    Ahmed, C.F., Tanbeer, S.K., Jeong, B.-S., Lee, Y.-K.: Efficient tree structures for high utility pattern mining in incremental databases. IEEE Trans. Knowl. Data Eng. 21(12), 1708–1721 (2009)CrossRefGoogle Scholar
  4. 4.
    Alves, R., Rodriguez-Baena, D.S., Aguilar-Ruiz, J.S.: Gene association analysis: a survey of frequent pattern mining from gene expression data. Brief. Bioinform. 11(2), 210–224 (2009)CrossRefGoogle Scholar
  5. 5.
    Andreopoulos, B., An, A., Wang, X., Schroeder, M.: A roadmap of clustering algorithms: finding a match for a biomedical application. Brief. Bioinform. 10(3), 297–314 (2009)CrossRefGoogle Scholar
  6. 6.
    BMSWebView1: SMPF: an open-source data mining library (2016). http://www.philippe-fournier-viger.com/spmf/index.php?link=datasets.php. Accessed 14 June 2016
  7. 7.
    Brijs, T., Swinnen, G., Vanhoof, K., Wets, G.: Using association rules for product assortment decisions: a case study. In: Knowledge Discovery and Data Mining, pp. 254–260 (1999)Google Scholar
  8. 8.
    Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: ACM SIGMOD Record, vol. 26, pp. 255–264. ACM (1997)Google Scholar
  9. 9.
    Chan, R.C., Yang, Q., Shen, Y.-D.: Mining high utility itemsets. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 19–26. IEEE (2003)Google Scholar
  10. 10.
    Chen, K., Liu, L.: The “Best k” for entropy-based categorical data clustering (2005)Google Scholar
  11. 11.
    Guha, S., Rastogi, R., Shim, K.: ROCK: a robust clustering algorithm for categorical attributes. In: Proceedings of 15th International Conference on Data Engineering, pp. 512–521. IEEE (1999)Google Scholar
  12. 12.
    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
  13. 13.
    Huang, Z.: Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discov. 2(3), 283–304 (1998)CrossRefGoogle Scholar
  14. 14.
    Lakhawat, P., Mishra, M., Somani, A.: A clustering based prediction scheme for high utility itemsets. In: Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, pp. 123–134. INSTICC, SciTePress (2017)Google Scholar
  15. 15.
    Lakhawat, P., Mishra, M., Somani, A.K.: A novel clustering algorithm to capture utility information in transactional data. In: KDIR, pp. 456–462 (2016)Google Scholar
  16. 16.
    Li, H.-F., Huang, H.-Y., Chen, Y.-C., Liu, Y.-J., Lee, S.-Y.: Fast and memory efficient mining of high utility itemsets in data streams. In: Eighth IEEE International Conference on Data Mining, ICDM 2008, pp. 881–886. IEEE (2008)Google Scholar
  17. 17.
    Liao, S.-H., Chu, P.-H., Hsiao, P.-Y.: Data mining techniques and applications-a decade review from 2000 to 2011. Expert. Syst. Appl. 39(12), 11303–11311 (2012)CrossRefGoogle Scholar
  18. 18.
    Liu, Y., Liao, W.-K., Choudhary, A.: A fast high utility itemsets mining algorithm. In: Proceedings of the 1st International Workshop on Utility-based Data Mining, pp. 90–99. ACM (2005)Google Scholar
  19. 19.
    Naulaerts, S., et al.: A primer to frequent itemset mining for bioinformatics. Brief. Bioinform. 16(2), 216–231 (2015)CrossRefGoogle Scholar
  20. 20.
    Ngai, E.W., Xiu, L., Chau, D.C.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert. Syst. Appl. 36(2), 2592–2602 (2009)CrossRefGoogle Scholar
  21. 21.
    RetailDataset: Frequent itemset mining dataset repository (2016). http://fimi.ua.ac.be/data/. Accessed 14 June 2016
  22. 22.
    Seabold, S., Perktold, J.: StatsModels: econometric and statistical modeling with python. In: 9th Python in Science Conference (2010)Google Scholar
  23. 23.
    Toivonen, H., et al.: Sampling large databases for association rules. VLDB 96, 134–145 (1996)Google Scholar
  24. 24.
    Tseng, V.S., Wu, C.-W., Fournier-Viger, P., Yu, P.S.: Efficient algorithms for mining the concise and lossless representation of high utility itemsets. IEEE Trans. Knowl. Data Eng. 27(3), 726–739 (2015)CrossRefGoogle Scholar
  25. 25.
    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
  26. 26.
    Yan, H., Chen, K., Liu, L., Yi, Z.: Scale: a scalable framework for efficiently clustering transactional data. Data Min. Knowl. Discov. 20(1), 1–27 (2010)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Zaki, M.J.: Scalable algorithms for association mining. IEEE Trans. Knowl. Data Eng. 12(3), 372–390 (2000)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Iowa State UniversityAmesUSA

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