Performance Evaluation of Methods for Mining Frequent Itemsets on Temporal Data

  • Tripti TripathiEmail author
  • Divakar Yadav
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 44)


Data mining is a method, used to extract usable and valuable information from the bulk of the data. Frequent data mining is an interesting task that is used to find frequent patterns from the database. In this paper, we used to perform frequent item set mining on temporal data. Temporal data contains data that primarily ranges over time. The idea of the time hierarchy is introduced to generate rules from temporal data. In this paper, we try to solve the problems using three popular data mining algorithms such as FP growth, Eclat and Apriori algorithm. The main focus of this study is to generate efficient algorithms that consume very less runtime and present the more frequent item set from the dataset. We evaluate our algorithms through experiments.


Data mining Frequent itemset mining Temporal data Time hierarchies 


  1. 1.
    Han, M.K., Pei, J.: Data mining: concepts and techniques. Elsevier, Amsterdam, The Netherlands (2011)Google Scholar
  2. 2.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993)CrossRefGoogle Scholar
  3. 3.
    Tsay, Y.-J., Hsu, T.-J., Yub, J.-R.: FIUT: a new method for mining frequent itemsets. Inf. Sci. 179, 1724–1737 (2009)Google Scholar
  4. 4.
    Ghorbani, M., Abessi, M.: A new methodology for mining frequent itemsets on temporal data. IEEE Trans. Eng. Manag. PP(99) (2017)Google Scholar
  5. 5.
    Veena, S., Rangarajan, P.: Optimization of active apriori algorithm using an effective genetic algorithm for the identification of top-l elements in a peer to peer network. Asian J. Appl. Sci. 2(5) (2014). ISSN 2321–0893Google Scholar
  6. 6.
    Zeng, Y., Yin, S., Liu, J., Zhang, M.: Research of improved FP-growth algorithm in association rules mining. Sci. Program. 2015, 6 (2015)Google Scholar
  7. 7.
    Kaur, M., Garg, U., Kaaur, S.: Advanced eclat algorithm for frequent itemsets generation. Int. J. Appl. Eng. Res. 10(9), 23263–23279 (2015)Google Scholar
  8. 8.
    Li, N., Zeng, L., He, Q., Shi, Z.: Parallel implementation of apriori algorithm based on mapreduce. In: 2012 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 236–241. IEEE (2012)Google Scholar
  9. 9.
    Aggarwal, C.C.: An introduction to frequent pattern mining. In: Aggarwal, C.C., Han, J. (eds.) Frequent pattern mining, pp. 1–14. Springer, Basel (2014)zbMATHGoogle Scholar
  10. 10.
    Essalmi, H., Far, M.E., Mohajir, M.E., Chahhou, M.: A novel approach for mining frequent itemsets: AprioriMin. In: 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 286– 289 (2016)Google Scholar
  11. 11.
  12. 12.
    Goyal, N., Jain, S.K.: A comparative study of different frequent pattern mining algorithm for uncertain data: a survey. In: 2016 International Conference on Computing, Communication and Automation (ICCCA), pp. 183–187. IEEE (2016)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringMMMUTGorakhpurIndia

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