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Performance Analysis of Mining Frequent Itemsets Based on Tree Structure Algorithm using Synthetic Dataset

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Author Affiliations

  • 1Faculty of Technology & Engineering, C. U. Shah University, Wadhwan City, Gujarat, India
  • 2RDI Centre, C. U. Shah University, Wadhwan City, Gujarat, India

Res. J. Computer & IT Sci., Volume 12, Issue (2), Pages 1-5, December,20 (2024)

Abstract

The most important data mining problem is mining of association rules. There are mainly two sub-problems, finding all frequent itemsets which is above threshold and finding association rules from generated frequent itemsets. The efficiency of algorithms is dependent on three factors: the candidates generation process, the structure is used and the implementation. All the previously available algorithms for mining frequent itemsets from Synthetic dataset are not efficient and scalable. The main aim of this paper is to presents a newly discovered Frequent Itemset Tree (FI-Tree) data structure. It is used for stowing frequent itemsets and its associated Transaction ID sets. In several data characteristics, MFIBT have a unique feature is that it has runs speedy. Large-scale experiments had been conducted and performance compared between several algorithms, a result shows that MFIBT better performs in terms of memory consumption and execution time on synthetic dataset. Also it is highly scalable in mining frequent itemsets from synthetic dataset.

References

  1. Agrawal, R., & Srikant, R. (1994)., Fast algorithms for mining association rules., In Proc. 20th int. conf. very large data bases, VLDB, 1215, 487-499.
  2. Borgelt, C. (2003)., Efficient implementations of apriori and eclat., In FIMI’03: Proceedings of the IEEE ICDM workshop on frequent itemset mining implementations (Vol. 90).
  3. Han, J., Pei, J., & Yin, Y. (2000)., Mining frequent patterns without candidate generation., ACM sigmod record, 29(2), 1-12.
  4. Borgelt, C. (2010)., Simple algorithms for frequent item set mining., In Advances in Machine Learning II: Dedicated to the Memory of Professor Ryszard S. Michalski, 351-369. Berlin, Heidelberg: Springer Berlin Heidelberg.
  5. Lan, Q., Zhang, D., & Wu, B. (2009)., A new algorithm for frequent.,
  6. Goethals, B. (2003)., Frequent itemset mining dataset repository., http://fimi. cs. helsinki. fi/data/.
  7. Bayardo, R. (2014)., Frequent itemset mining dataset repository., UCI datasets and PUMSB.