(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Computer Research (IJACR)

ISSN (Print):2249-7277    ISSN (Online):2277-7970
Volume-13 Issue-62 March-2023
Full-Text PDF
Paper Title : An analysis and literature review of algorithms for frequent itemset mining
Author Name : Mrinabh Kumar and Animesh Kumar Dubey
Abstract :

The data mining process should be led by domain knowledge. It includes different aspects including the selection of the data, interpretation, extraction, and transformation. In this paper different domains have been covered for the analysis of various data mining algorithms. The main emphasis on the algorithms which are mainly used for the extraction and discovering of interesting patterns and relationships. Various data mining algorithms, such as sequential pattern discovery using equivalence classes (SPADE), k-means, Apriori algorithm, FP-Growth and others, were discussed in this paper. The reviews and analysis of the advantages and disadvantages of various data mining approaches have been explored with advantages and limitations. In summary, this paper provides a comprehensive understanding of data mining approaches and their potential applications in various fields.

Keywords : Data mining, Domain knowledge, Preprocessing, Knowledge discovery.
Cite this article : Kumar M, Dubey AK. An analysis and literature review of algorithms for frequent itemset mining. International Journal of Advanced Computer Research. 2023; 13(62):1-7. DOI:10.19101/IJACR.2023.1362001.
References :
[1]Shabtay L, Fournier-Viger P, Yaari R, Dattner I. A guided FP-Growth algorithm for mining multitude-targeted item-sets and class association rules in imbalanced data. Information Sciences. 2021; 553:353-75.
[Crossref] [Google Scholar]
[2]Shawkat M, Badawi M, El-ghamrawy S, Arnous R, El-desoky A. An optimized FP-growth algorithm for discovery of association rules. The Journal of Supercomputing. 2022:1-28.
[Crossref] [Google Scholar]
[3]Ghosh M, Roy A, Sil P, Mondal KC. Frequent itemset mining using FP-tree: a CLA-based approach and its extended application in biodiversity data. Innovations in Systems and Software Engineering. 2022:1-9.
[Crossref] [Google Scholar]
[4]Zhang X, Tang Y, Liu Q, Liu G, Ning X, Chen J. A fault analysis method based on association rule mining for distribution terminal unit. Applied Sciences. 2021; 11(11):5221.
[Crossref] [Google Scholar]
[5]Dubey AK, Shandilya SK. A novel J2ME service for mining incremental patterns in mobile computing. In information and communication technologies: international conference, ICT 2010, Kochi, Kerala, India, Proceedings 2010 (pp. 157-64). Springer Berlin Heidelberg.
[Crossref] [Google Scholar]
[6]Happawana KA, Diamond BJ. Association rule learning in neuropsychological data analysis for Alzheimer’s disease. Journal of Neuropsychology. 2022; 16(1):116-30.
[Crossref] [Google Scholar]
[7]Alcan D, Ozdemir K, Ozkan B, Mucan AY, Ozcan T. A comparative analysis of Apriori and FP-growth algorithms for market basket analysis using multi-level association rule mining. In industrial engineering in the Covid-19 Era: selected papers from the hybrid global joint conference on industrial engineering and its application areas, GJCIE 2022, October 29-30, 2022 2023 (pp. 128-37). Cham: Springer Nature Switzerland.
[Crossref] [Google Scholar]
[8]Shahin M, Inoubli W, Shah SA, Yahia SB, Draheim D. Distributed scalable association rule mining over covid-19 data. In future data and security engineering: 8th international conference, FDSE 2021, Virtual Event, 2021, Proceedings 2021 (pp. 39-52). Cham: Springer International Publishing.
[Crossref] [Google Scholar]
[9]Dubey AK, Shandilya SK. Exploiting need of data mining services in mobile computing environments. In international conference on computational intelligence and communication networks 2010 (pp. 409-14). IEEE.
[Crossref] [Google Scholar]
[10]Dubey AK, Gupta U, Jain S. Computational measure of cancer using data mining and optimization. In sustainable communication networks and application 2019 (pp. 626-32). Springer International Publishing.
[Crossref] [Google Scholar]
[11]Ghafoor N, Ahmad M. Nazish Ghafoor, Mansoor ahmad prioritizing effectiveness of algorithms of association rule mining. Journal of Computational Learning Strategies & Practices. 2021; 1(1):18-30.
[Google Scholar]
[12]Fernandez-Basso C, Ruiz MD, Martin-Bautista MJ. New spark solutions for distributed frequent itemset and association rule mining algorithms. Cluster Computing. 2023:1-8.
[Crossref] [Google Scholar]
[13]Makkar K, Kumar P, Poriye M, Aggarwal S. Improvisation in opinion mining using data preprocessing techniques based on consumer’s review. International Journal of Advanced Technology and Engineering Exploration. 2023; 10(99):257-77.
[Crossref] [Google Scholar]
[14]Dubey AK, Kapoor D, Kashyap V. A review on performance analysis of data mining methods in IoT. International Journal of Advanced Technology and Engineering Exploration. 2020; 7(73):193-200.
[Crossref] [Google Scholar]
[15]Suhandi N, Gustriansyah R. Marketing strategy using frequent pattern growth. Journal of Computer Networks, Architecture and High Performance Computing. 2021; 3(2):194-201.
[Crossref] [Google Scholar]
[16]Saxena A, Rajpoot V. A comparative analysis of association rule mining algorithms. In IOP conference series: materials science and engineering 2021 (pp. 1-11). IOP Publishing.
[Crossref] [Google Scholar]
[17]Babu MV, Sreedevi M. Performance analysis on advances in frequent pattern growth algorithm. In 2022 international conference on advances in computing, communication and applied informatics 2022 (pp. 1-5). IEEE.
[Google Scholar]
[18]Anupama CG, Lakshmi C. Approaches to parallelise Eclat algorithm and analysing its performance for K length prefix-based equivalence classes. International Journal of Business Intelligence and Data Mining. 2023; 22(1-2):34-48.
[Crossref] [Google Scholar]
[19]Nikitin E, Kashevnik A, Shilov N. Shopping basket analisys for mining equipment: comparison and evaluation of modern methods. In 2022 31st conference of open innovations association 2022 (pp. 207-13). IEEE.
[Crossref] [Google Scholar]
[21]Yogasini M, Prathibha BN. Comparative analysis on frequent Itemset mining algorithms in vertically partitioned cloud data. In futuristic communication and network technologies: select proceedings of VICFCNT 2020 (pp. 395-402). Springer Singapore.
[Crossref] [Google Scholar]
[21]Zhang F, Zhang Y, Liao X, Jin H. PNPFI: an efficient parallel frequent itemsets mining algorithm. In 22nd international conference on computer supported cooperative work in design 2018 (pp. 172-7). IEEE.
[Crossref] [Google Scholar]
[22]Agarwal R, Gautam A, Saksena AK, Rai A, Karatangi SV. Method for mining frequent item sets considering average utility. In international conference on emerging smart computing and informatics 2021 (pp. 275-8). IEEE.
[Crossref] [Google Scholar]
[23]Amballoor RG, Naik SB. Utility-based frequent itemsets in data streams using sliding window. In international conference on computing, communication, and intelligent systems 2021 (pp. 108-12). IEEE.
[Crossref] [Google Scholar]
[24]Bhatia J, Gupta A. Association rule mining by discretization of agricultural data using extended partitioning algorithm. In 6th international conference for convergence in technology 2021(pp. 1-6). IEEE.
[Crossref] [Google Scholar]
[25]Cao H, Yang S, Wang Q, Wang Q, Zhang L. A closed itemset property based multi-objective evolutionary approach for mining frequent and high utility itemsets. In congress on evolutionary computation 2019 (pp. 3356-63). IEEE.
[Crossref] [Google Scholar]
[26]Fang W, Zhang Q, Sun J, Wu X. Mining high quality patterns using multi-objective evolutionary algorithm. IEEE Transactions on Knowledge and Data Engineering. 2020; 34(8):3883-98.
[Crossref] [Google Scholar]
[27]Halim Z, Ali O, Khan MG. On the efficient representation of datasets as graphs to mine maximal frequent itemsets. IEEE Transactions on Knowledge and Data Engineering. 2019; 33(4):1674-91.
[Crossref] [Google Scholar]
[28]Hong TP, Huang WM, Lan GC, Chiang MC, Lin JC. A bitmap approach for mining erasable itemsets. IEEE Access. 2021; 9:106029-38.
[Crossref] [Google Scholar]
[29]Junrui Y, Jingyi Y. Frequent itemsets mining algorithm for uncertain data streams based on triangular matrix. In international conference on power electronics, computer applications 2021 (pp. 327-30). IEEE.
[Crossref] [Google Scholar]
[30]Nalousi S, Farhang Y, Sangar AB. Weighted frequent itemset mining using weighted subtrees: WST-WFIM. IEEE Canadian Journal of Electrical and Computer Engineering. 2021; 44(2):206-15.
[Crossref] [Google Scholar]
[31]Qu JF, Hang B, Wu Z, Wu Z, Gu Q, Tang B. Efficient mining of frequent itemsets using only one dynamic prefix tree. IEEE Access. 2020; 8:183722-35.
[Crossref] [Google Scholar]
[32]Thurachon W, Kreesuradej W. Incremental association rule mining with a fast incremental updating frequent pattern growth algorithm. IEEE Access. 2021; 9:55726-41.
[Crossref] [Google Scholar]
[33]Wu C, Jiang H. Research on parallelization of frequent itemsets mining algorithm. In 6th international conference on cloud computing and big data analytics 2021 (pp. 210-215). IEEE.
[Crossref] [Google Scholar]
[34]De la Cruz-Ruiz F, Canul-Reich J, Rivera-López R, De la Cruz-Hernández E. Impact of data balancing a multiclass dataset before the creation of association rules to study bacterial vaginosis. Intelligent Medicine. 2023.
[Crossref] [Google Scholar]
[35]Islam MA, Majumder MZ, Hussein MA. Chronic kidney disease prediction based on machine learning algorithms. Journal of Pathology Informatics. 2023.
[Crossref] [Google Scholar]
[36]Ho GT, Tsang YP, Wu Q, Tang V. Ck-FARM: an R package to discover big data associations for business intelligence. SoftwareX. 2023.
[Google Scholar]