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Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
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Developing an Efficient Mining of Frequent Itemsets using OFIM for Big Data

by Abdulkader M. Al-Badani, Abdualmajed A. Al-Khulaidi
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 45
Year of Publication: 2024
Authors: Abdulkader M. Al-Badani, Abdualmajed A. Al-Khulaidi
10.5120/ijais2024451980

Abdulkader M. Al-Badani, Abdualmajed A. Al-Khulaidi . Developing an Efficient Mining of Frequent Itemsets using OFIM for Big Data. International Journal of Applied Information Systems. 12, 45 ( Jul 2024), 16-22. DOI=10.5120/ijais2024451980

@article{ 10.5120/ijais2024451980,
author = { Abdulkader M. Al-Badani, Abdualmajed A. Al-Khulaidi },
title = { Developing an Efficient Mining of Frequent Itemsets using OFIM for Big Data },
journal = { International Journal of Applied Information Systems },
issue_date = { Jul 2024 },
volume = { 12 },
number = { 45 },
month = { Jul },
year = { 2024 },
issn = { 2249-0868 },
pages = { 16-22 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number45/developing-an-efficient-mining-of-frequent-itemsets-using-ofim-for-big-data/ },
doi = { 10.5120/ijais2024451980 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-07-18T00:40:46.285456+05:30
%A Abdulkader M. Al-Badani
%A Abdualmajed A. Al-Khulaidi
%T Developing an Efficient Mining of Frequent Itemsets using OFIM for Big Data
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 45
%P 16-22
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Big data mining is challenging.An effective algorithm and computer software are needed to solve problems while working with large datasets.The FP Growth Algorithm takes a long time to compute and extract results, and it demands a lot of memory.Right now, the FP_Growth algorithm is among the finest methods for mining frequent itemsets.The transaction dataset is used to create a tree structure, which is then recursively traversed to extract frequently occurring itemsets using a depth first search strategy.Additionally, creating an FP_tree requires time and suffers from growing larger FP_trees and producing a high number of frequent itemsets.In this paper, the suggest alterations to the FP_Growth algorithm's operation.With our usage of the proposed matrix OFIM to build a very compact FP-tree, the recommended approach would cut mining time and the number of regularly generated items, giving a considerable reduction in decision_making in large datasets.Furthermore, our technique significantly improves its speed in handling large datasets by limiting the amount of items that are produced often, thereby optimizing memory use.

References
  1. S. P. Tamba, M. Sitanggang, B. C. Situmorang, G. L. Panjaitan, and M. Nababan. 2022. “Application of data mining to determine the level of fish sales in pt. trans retail with fp-growth method,” INFOKUM, pp. 905–913.
  2. T. Patil, R. Rana, and P. Singh. 2022. “Distributed frequent pattern analysis in big data.”International Research Journal of Modernization in Engineering Technology and Science ,pp.1-3.
  3. A. Ayu, A. P. Windarto, and D. Suhendro, 2021 .“Implementasi data mining dengan metode fp-growth terhadap data penjualan barang sebagai strategi penjualan pada cv. a & a copier,” Resolusi: Rekayasa Teknik Informatika dan Informasi, pp. 67–75.
  4. A. Siswandi, A. S. Sunge, and R. Y. Wulandari. 2018.“Analisa data mining dengan metode klasifikasi untuk produk cacat pada pt. shuangying international indonesia,” Jurnal SIGMA, pp. 153–156.
  5. B. Anwar, A. Ambiyar, and F. Fadhilah, 2023.“Application of the fp-growth method to determine drug sales patterns,” Sinkron: jurnal dan penelitian teknik informatika, pp. 405–414.
  6. M. M. Hasan and S. Z. Mishu. 2018. “An adaptive method for mining frequent itemsets based on apriori and fp growth algorithm,” in 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2). IEEE, pp. 1–4.
  7. Almira, A., Suendri, S., & Ikhwan, A.2021. Implementasi Data Mining Menggunakan Algoritma Fp-Growth pada Analisis Pola Pencurian Daya Listrik. Jurnal Informatika Universitas Pamulang, 6(2), pp.442-448.‏
  8. J. Han, J. Pei, and Y. Yin. 2000. “Mining frequent patterns without candidate generation,” ACM sigmod record, no. 2, pp. 1– 12.
  9. F. Wei and L. Xiang. 2015. “Improved frequent pattern mining algorithm based on fp-tree,” in Proceedings of The Fourth International Conference on Information Science and Cloud Computing (ISCC2015), pp. 18–19.
  10. R. Krupali, D. Garg, and K. 2017. Kotecha, “An improved approach of fp-growth tree for frequent itemset mining using partition projection and parallel projection techniques,” International Recent and Innovation Trends in Computing and Communication, pp. 929–934.
  11. M. Shridhar and M. 2017. Parmar, “Survey on association rule mining and its approaches,” Int J Comput Sci Eng, no. 3, pp. 129–135.
  12. H. Khanali and B. Vaziri. 2017. “A survey on improved algorithms for mining association rules,” Int. J. Comput. Appl, p. 8887.
  13. J. Lu, W. Xu, K. Zhou, and Z. Guo. 2023. “Frequent itemset mining algorithm based on linear table,” Journal of Database Management (JDM), pp. 1–21.
  14. M. Barkhan, R. Ramazani, and A. Kabani. 2022. “An algorithm to create sorted fp-growth tree for extracting association rules,” Research square, pp. 1–9.
  15. M. K. Sohrabi and M. H. HASANNEJAD. 2016. “Association rule mining using new fp-linked list algorithm,” Journal of Advances in Computer Research , pp. 23–34.
  16. T. Patil, R. Rana, and P. Singh. 2022. “Distributed frequent pattern analysis in big data.”International Research Journal of Modernization in Engineering Technology and Science ,pp.1-3.
  17. K. BHARATHI and D. B. DEVENDER. 2020. “Frequent itemset mining from big data using fp-growth algorithm,” Complexity International Journal (CIJ) , pp. 582–591.
  18. B. Zhang. 2021. “Optimization of fp-growth algorithm based on cloud computing and computer big data,” International Journal of System Assurance Engineering and Management, pp. 853–863.
  19. S. X. Le Zhang, X. Li, X. Wu, and P.-C. Chang. 2019. “An improved fp-growth algorithm based on projection database mining in big data,” Journal of Information Hiding and Multimedia Signal Processing, pp. 81–90.
  20. M. El Hadi Benelhadj, M. M. Deye, and Y. Slimani. 2023. “Signaturebased tree for finding frequent itemsets,” Journal of Communications Software and Systems, pp. 70–80.
  21. S. Bhise and S. Kale. 2017. “Effieient algorithms to find frequent itemset using data mining,” Int. Res. J. Eng. Technol., pp. 2645–2648.
  22. A. S. Alhegami and H. A. Alsaeedi. 2020. “A framework for incremental parallel mining of interesting association patterns for big data,” International Journal of Computing, pp. 106–117.
  23. H. A. Alsaeedi and A. S. Alhegami. 2022. “An incremental interesting maximal frequent itemset mining based on fp-growth algorithm,” Complexity.
  24. J.Han, J.Pei, & Y.Yin. 2000. "Mining frequent patterns without candidate generation." ACM sigmod record , pp. 1-12.
  25. F.Wei, & L. Xiang. 2015. "Improved frequent pattern mining algorithm based on FP-Tree. "In Proceedings of The Fourth International Conference on Information Science and Cloud Computing (ISCC2015), pp. 18-19.‏
  26. R.Krupali, D.Garg , & K. Kotecha. 2017." An improved approach of FP-Growth tree for frequent itemset mining using partition projection and parallel projection techniques." International Recent and Innovation Trends in Computing and Communication,pp. 929-934.
  27. C. J. Merz, “Uci repository of machine learning databases,” URL: http://www. ics. uci. edu/˜ mlearn/MLRepository. html, 1998.‏
Index Terms

Computer Science
Information Sciences
Data Mining
Association Rule
Frequent Itemsets Mining

Keywords

FP-Growth Algorithm Aprioiri Algorithm FP-tree Support Count Ordered Frequent Itemset Matrix

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