Abstract
Analyzing many high-utility itemsets using HUIM methods is challenging and time-consuming. To solve this issue, Closed High-Utility Itemsets (CHUI), a condensed and lossless representation of high-utility itemsets, have been proposed.
Using a matrix to hold both the total value of the transactions and the value of each item in transactions, we describe a new approach for the CHUI problem in this work. By merging the items and removing unnecessary values, one can use this matrix to enhance the mining of closed high-utility itemsets. The pruning approach in the suggested algorithm compares the aggregate value of the items with the minimum value, which can result in a more effective use of time and space. In contrast to other algorithms, which could only establish a minimum value for all the items, this method can set a minimum value for each item, which is one of its surprises. In addition to only scanning the database once, this approach is more straightforward and up to three times more time- and space-efficient than the prior algorithms.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agrawal R, Srikant R (1994) “Fast algorithms for mining association rules.” Proc 20th Int Conf Very Large Data Bases VLDB 1215:487–499
Yao H, Hamilton HJ, Butz CJ (2004) “A foundational approach to mining itemset utilities from databases”. Proceedings of the 2004 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics
Ryang H, Yun U (2016) High utility pattern mining over data streams with sliding window technique. Expert Syst Appl 57:214–231
Wu CW et al. (2015) “Mining closed+ high utility itemsets without candidate generation”. 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE
Yun U et al (2018) Damped window based high average utility pattern mining over data streams. Knowl-Based Syst 144:188–205
Yun U, Lee G, Yoon E (2017) Efficient high utility pattern mining for establishing manufacturing plans with sliding window control. IEEE Trans Industr Electron 64(9):7239–7249
Zida S et al (2017) EFIM: a fast and memory efficient algorithm for high-utility itemset mining. Knowl Inf Syst 51(2):595–625
Dahiya V, Dalal S (2022) EAHUIM: enhanced absolute high utility itemset miner for big data. Int J Inf Manag Data Insights 2(1):100055
Fasihy H, Shahraki MHN (2018) Incremental mining maximal frequent patterns from univariate uncertain data. Knowl-Based Syst 152:40–50
Rezaul Karim Md et al (2018) Mining maximal frequent patterns in transactional databases and dynamic data streams: a spark-based approach. Inf Sci 432:278–300
Li H-F, Huang H-Y, Lee S-Y (2011) Fast and memory efficient mining of high-utility itemsets from data streams: with and without negative item profits. Knowl Inf Syst 28(3):495–522
Vo B et al (2017) A novel approach for mining maximal frequent patterns. Expert Syst Appl 73:178–186
Yun U, Lee G (2016) Incremental mining of weighted maximal frequent itemsets from dynamic databases. Expert Syst Appl 54:304–327
Tseng VS et al (2014) Efficient algorithms for mining the concise and lossless representation of high utility itemsets. IEEE Trans Knowl Data Eng 27(3):726–739
Liu M, Qu J (2012) “Mining high utility itemsets without candidate generation”. Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Peng AY, Koh YS, Riddle P (2017) “mHUIMiner: A fast high utility itemset mining algorithm for sparse datasets”. Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, Cham
Dam TL et al (2019) CLS-Miner: efficient and effective closed high-utility itemset mining. Front Comput Sci 13(2):357–381
Wei T et al (2020) FCHUIM: efficient frequent and closed high-utility itemsets mining. IEEE Access 8:109928–109939
Fournier-Viger P et al. (2014) “FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning”. International Symposium on Methodologies for Intelligent Systems. Springer, Cham
Singh K et al (2018) CHN: an efficient algorithm for mining closed high utility itemsets with negative utility. IEEE Trans Knowl Data Eng 48(1859):1877
Dawar S, Sharma V, Goyal V (2017) Mining top-k high-utility itemsets from a data stream under sliding window model. Appl Intell 47(4):1240–1255
Zihayat M, An A (2014) Mining top-k high utility patterns over data streams. Inf Sci 285:138–161
Duong QH et al (2018) Efficient high utility itemset mining using buffered utility-lists. Appl Intell 48(7):1859–1877
Nguyen LT et al (2019) An efficient method for mining high utility closed itemsets. Inf Sci 495:78–99
Sahoo J, Das AK, Goswami A (2016) An efficient fast algorithm for discovering closed+ high utility itemsets. Appl Intell 45(1):44–74
Sohrabi MK (2020) An efficient projection-based method for high utility itemset mining using a novel pruning approach on the utility matrix. Knowl Inf Syst 62(11):4141–4167
Lin JCW, Djenouri Y, Srivastava G (2021) Efficient closed high-utility pattern fusion model in large-scale databases. Inf Fusion 76:122–132
Lin JCW et al (2021) A predictive GA-based model for closed high-utility itemset mining. Appl Soft Comput 108:107422
Jena M, Dehuri S (2019) “Entropy based bayes’ rule for coping dimensionality reduction in predictive task of data mining”. 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP). IEEE
Jena M, Dehuri S (2022) An integrated novel framework for coping missing values imputation and classification. IEEE Access 10:69373–69387
Jena M, Kabi B, Dehuri S (2022) A faster lazy learner for data science. Int J Inf Technol 14(4):2119–2128
Acknowledgments
This work was supported by Shahid Rajaee Teacher Training University.
Author information
Authors and Affiliations
Contributions
All the authors contributed to the study’s conception and design.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interests.
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mofid, A.H., Daneshpour, N. & Torabi, Z. MMC: efficient and effective closed high-utility itemset mining. J Supercomput 80, 18900–18918 (2024). https://doi.org/10.1007/s11227-024-06224-4
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-024-06224-4