research-article

Mining High Utility Itemsets with Hill Climbing and Simulated Annealing

Authors:

M. Saqib Nawaz,

Philippe Fournier-Viger,

Wei SongAuthors Info & Claims

ACM Transactions on Management Information System (TMIS), Volume 13, Issue 1

Article No.: 4, Pages 1 - 22

https://doi.org/10.1145/3462636

Published: 05 October 2021 Publication History

Abstract

High utility itemset mining (HUIM) is the task of finding all items set, purchased together, that generate a high profit in a transaction database. In the past, several algorithms have been developed to mine high utility itemsets (HUIs). However, most of them cannot properly handle the exponential search space while finding HUIs when the size of the database and total number of items increases. Recently, evolutionary and heuristic algorithms were designed to mine HUIs, which provided considerable performance improvement. However, they can still have a long runtime and some may miss many HUIs. To address this problem, this article proposes two algorithms for HUIM based on Hill Climbing (HUIM-HC) and Simulated Annealing (HUIM-SA). Both algorithms transform the input database into a bitmap for efficient utility computation and for search space pruning. To improve population diversity, HUIs discovered by evolution are used as target values for the next population instead of keeping the current optimal values in the next population. Through experiments on real-life datasets, it was found that the proposed algorithms are faster than state-of-the-art heuristic and evolutionary HUIM algorithms, that HUIM-SA discovers similar HUIs, and that HUIM-SA evolves linearly with the number of iterations.

References

[1]

P. Fournier-Viger, J. C. W. Lin, R. U. Kiran, Y. S. Koh, and R. Thomas. 2017. A survey of sequential pattern mining. Data Sci. Patt. Recog. 1, 1 (2017), 54–77.

[2]

J. M. Luna, P. Fournier-Viger, and S. Ventura. 2019. Frequent itemset mining: A 25 years review. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 9, 6 (2019), e1329.

[3]

C. Zhang and S. Zhang. 2002. Association Rule Mining, Models and Algorithms. Springer.

Digital Library

[4]

P. Fournier-Viger, J. C. W. Lin, T. Truong-Chi, and R. Nkambou. 2019. A. Survey of high utility itemset mining. In High-Utility Pattern Mining: Theory, Algorithms and Applications. Springer, 1–45.

[5]

L. Ni, W. Luo, N. Lu, and W. Zhu. 2020. Mining the local dependency itemset in a products network. ACM Trans. Manag. Inf. Syst. 11, 1 (2020), Article 3, 31 pages.

Digital Library

[6]

Mo Zihayat, H. Davoudi, and A. An2016. Top-k utility-based gene regulation sequential pattern discovery. In Proceedings of the International Conference on Bioinformatics and Biomedicine. 266–273.

[7]

B. E. Shie, J. H. Cheng, K. T. Chuang, and V. S. Tseng. 2012. A. One-phase method for mining high utility mobile sequential patterns in mobile commerce environments. In Proceedings of the International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems. 616–626.

Digital Library

[8]

W. Gan, J. C. W. Lin, H. C. Chao, P. Fournier-Viger, X. Wang, and P. S. Yu. 2020.Utility-driven mining of trend information for intelligent system. ACM Trans Manag. Inf. Syst. 11, 3 (2020), Article 14, 28 pages.

Digital Library

[9]

Y. Liu, W. Liao, and A. N. Choudhary. 2005. A two-phase algorithm for fast discovery of high utility itemsets. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 689–695.

Digital Library

[10]

C. F. Ahmed, S. K. Tanbeer, B. Jeong, and Y. Lee. 2009. Efficient tree structures for high utility pattern mining in incremental databases. IEEE Trans. Knowl. Data. Eng. 21, 12 (2009), 1708–1721.

Digital Library

[11]

V. S. Tseng, C. Wu, B. Shie, and P. S. Yu.2010. UP-Growth: An efficient algorithm for high utility itemset mining. In Proceedings of the International Conference on Knowledge Discovery and Data Mining. 253–262.

Digital Library

[12]

V. S. Tseng, C. Wu, P. Fournier-Viger, and P. S. Yu.2016. Efficient algorithms for mining top-k high utility itemsets. IEEE Trans. Knowl. Data. Eng. 28, 1 (2016), 54–67.

Digital Library

[13]

P. Fournier-Viger, C. Wu, S. Zida, and V. S. Tseng. 2014. FHM: Faster high-utility itemset mining using estimated utility co-occurrence pruning. In Proceedings of the International Symposium on Foundations of Intelligent Systems. 83–92.

[14]

S. Zida, P. Fournier-Viger, J. C. W. Lin, C. Wu, and V. S. Tseng. 2015. EFIM: A highly efficient algorithm for high-utility itemset mining. In Proceedings of the Mexican International Conference on Artificial Intelligence. 530–546.

[15]

S. Ventura and J. M. Luna. 2016. Pattern Mining with Evolutionary Algorithms. Springer.

Digital Library

[16]

J. M. Luna, M. Pechenizkiy, M. J. del Jesus, and S. Ventura. 2017. Mining context-aware association rules using grammar-based genetic programming. IEEE Trans. Cyber. 48, 11 (2017), 3030–3044.

[17]

X. Yu and M. Gen2010. Introduction to Evolutionary Algorithms. Springer.

Digital Library

[18]

S. Kannimuthu and K. Premalatha. 2014. Discovery of high utility itemsets using genetic algorithm with ranked mutation. Appl. Artif. Intell. 28, 4 (2014), 337–359.

Digital Library

[19]

J. C. W. Lin, L. Yang, P. Fournier-Viger, T. Hong, and M. Voznak. 2017. A binary PSO approach to mine high-utility itemsets. Soft Comput. 21, 17 (2017), 5103–5121.

Digital Library

[20]

J. C. W. Lin, L. Yang, P. Fournier-Viger, J. M. Wu, T. Hong, S. L. Wang, and J. Zhan. 2016. Mining high-utility itemsets based on particle swarm optimization. Eng. Appl. Artif. Intell. 55 (2016), 320–330.

Digital Library

[21]

K. E. Heraguemi, N. Kamel, and H. Drias. 2014. Association rule mining based on Bat algorithm. In Proceedings of the International Conference on Bio-Inspired Computing—Theories and Applications. 182–186.

[22]

K. E. Heraguemi, N. Kamel, and H. Drias. 2016. Multi-swarm Bat algorithm for association rule mining using multiple cooperative strategies. Appl. Intell. 45, 4 (2016), 1021–1033.

[23]

W. Song and C. Huang. 2018. Mining high utility itemsets using bio-inspired algorithms: A diverse optimal value framework. IEEE Access 6 (2018), 19568–19582.

[24]

W. Song and C. Huang. 2018. Discovering high utility itemsets based on the artificial bee colony algorithm. In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 3–14.

Digital Library

[25]

S. J. Russell and P. Norvig. 2010. Artificial Intelligence: A Modern Approach (3rd ed.). Pearson Education.

Digital Library

[26]

S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. 1983. Optimization by simulated annealing. Science 220, 4598 (1983), 671–680.

[27]

R. Agrawal and R. Srikant. 1994. Fast algorithms for mining association rules. In Proceedings of the International Conference on Very Large Data Bases. 487–499.

Digital Library

[28]

R. Chan R, Q. Yang, and Y. D. Shen. 2003. Mining high utility itemsets. In Proceedings of the International Conference on Data Mining. 19–26.

Digital Library

[29]

W. Song, Y. Liu, and J. Li.2014. BAHUI: Fast and memory efficient mining of high utility itemsets based on bitmap. Int. J. Data Warehouse Min. 10, 1 (2014), 1–15.

Digital Library

[30]

W. Song, Y. Liu, and J. Li.2014. BAHUI: Fast and memory efficient mining of high utility itemsets based on bitmap. Int. J. Data Warehouse Min. 10, 1 (2014), 1–15.

Digital Library

[31]

S. Bagui and P. Stanley. 2020. Mining frequent itemsets from streaming transaction data using genetic algorithms. J. Big Data 7, 1 (3030), 54.

[32]

Y. Djenouri, D. Djenouri, A. Belhadi, P. Fournier-Viger, and J. C. W. Lin. 2018.A new framework for meta heuristic-based frequent itemset mining. Appl Intell. 48, 12 (2018), 4775–4791.

Digital Library

[33]

D. Martín, J. Alcalá-Fdez, A. Rosete, and F. Herrera. 2016. NICGAR: A niching genetic algorithm to mine a diverse set of interesting quantitative association rules. Inf. Sci. 355-356 (2016), 208–228.

Digital Library

[34]

E. Alatas and E. Akin. 2006. An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules. Soft Comput. 10, 3 (2006), 230–237.

Digital Library

[35]

J. Alcala-Fdez, N. F. Pape, A. Bonarini, and F. Herrera. 2010. Analysis of the effectiveness of the genetic algorithms based on extraction of association rules. Fundam. Inform. 98, 1 (2010), 1–14.

Digital Library

[36]

S. Dehuri, S. Patnaik, A. Ghosh, and R. R. Mall. 2008. Application of elitist multi-objective genetic algorithm for classification rule generation. Appl. Soft Comput. 8, 1 (2008), 477–487.

Digital Library

[37]

P. P. Wakabi-Waiswa, V. Baryamureeba, and K. Sarukesi. 2011. Optimized association rule mining with genetic algorithms. In Proceedings of the International Conference on Natural Computation. 1116–1120.

[38]

X. Yan, X. Zhang, and X. Zhang. 2009. Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. Expert Syst. Appl. 36, 2 (2009), 3066–3076.

Digital Library

[39]

R. Pears and K. S. Koh. 2011. Weighted association rule mining using particle swarm optimization. In Proceedings of the International Workshop on New Frontiers in Applied Data Mining. 327–338.

Digital Library

[40]

J. Gou, F. Wang, and W. Luo. 2015. Mining fuzzy association rules based on parallel particle swarm optimization algorithm. Intell. Autom. Soft. Comput. 21, 2 (2015), 147–162.

[41]

Q. Zhang, W. Fang, J. Sun, and Q. Wang. 2019. Improved genetic algorithm for high-utility itemset mining. IEEE Access 7 (2019), 176799–176813.

[42]

J. M. T. Wu, J. Zhan, and J. C. W. Lin. 2017. An ACO-based approach to mine high-utility itemsets. Knowl. Based Syst. 116 (2017), 102–113.

Digital Library

[43]

W. Song and C. Huang. 2020. Mining high average-utility itemsets based on particle swarm optimization. Data Sci. Patt. Recog. 4, 2 (2020), 19–32.

[44]

N. Pazhaniraja, S. Sountharrajan, and B. S. Kumar. 2020. High utility itemset mining: A Boolean operators-based modified grey wolf optimization algorithm. Soft Comput. 24, 21 (2020), 16691–16704.

Digital Library

[45]

H. Yao, H. J. Hamilton, and C. J. Butz. 2004. A foundational approach to mining itemset utilities from databases. In Proceedings of the SIAM International Conference on Data Mining. 482–486.

[46]

H. Yao and H. J. Hamilton. 2006. Mining itemset utilities from transaction databases. Data Knowl. Eng. 59, 3 (2006), 603–626.

Digital Library

[47]

M. S. Nawaz, M. Z. Nawaz, O. Hasan, P. Fournier-Viger, and M. Sun. 2021. An evolutionary/heuristic-based proof searching framework for interactive theorem prover. Appl. Soft Comput. 104 (2021), 107200.

[48]

P. Fournier-Viger, J. C. W. Lin, A. Gomariz, T. Gueniche, A. Soltani, Z. Deng, and T. H. Lam. 2016. The SPMF open-source data mining library version 2. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 36–40.

[49]

T. Jones. 1995. Crossover, macromutation, and population-based search. In Proceedings of the International Conference on Genetic Algorithms. 73–80.

Digital Library

[50]

M. S. Nawaz, P. Fournier-Viger, W. Song, J. C. W. Lin, and B. Noack2021. Investigating crossover operators in genetic algorithms for high-utility itemset mining. In Proceedings of the Asian Conference on Intelligent Information and Database Systems. 16–28.

[51]

J. Grobler and A. P. Engelbrecht. 2016. Headless chicken particle swarm optimization algorithms. In Proceedings of the International Conference on Swarm Intelligence. 350–357.

[52]

T. P. Hong, C. H. Lee, and S. L. Wang. 2009. Mining high average-utility itemsets. In Proceedings of the International Conference on Systems, Man, and Cybernetics. 2526–2530.

Cited By

K LS SS SS A(2024)High Utility Itemset Extraction using PSO with Online Control Parameter CalibrationInternational Journal of Next-Generation Computing10.47164/ijngc.v15i1.1643Online publication date: 14-May-2024
https://doi.org/10.47164/ijngc.v15i1.1643
Gao ZHan MLiu SLi AMu D(2024)High utility itemsets mining based on hybrid harris hawk optimization and beluga whale optimization algorithmsJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23679346:4(7567-7602)Online publication date: 18-Apr-2024
https://doi.org/10.3233/JIFS-236793
Jiang HWu GSun MLi FSun YFang W(2024)PHUI-GA: GPU-Based Efficiency Evolutionary Algorithm for Mining High Utility ItemsetsJournal of Systems Engineering and Electronics10.23919/JSEE.2024.00002035:4(965-975)Online publication date: Aug-2024
https://doi.org/10.23919/JSEE.2024.000020
Show More Cited By

Index Terms

Mining High Utility Itemsets with Hill Climbing and Simulated Annealing
1. Information systems
  1. Information systems applications
    1. Data mining
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms
        Simulated annealing

Recommendations

High average-utility itemsets mining: a survey
Abstract
HUIM (High utility itemsets mining) is a sub-division of data mining dealing with the task to obtain promising patterns in the quantitative datasets. A variant of HUIM is to discover the HAUIM (High average-utility itemsets mining) where average-...
Mining High Transaction-Weighted Utility Itemsets
ICCEA '10: Proceedings of the 2010 Second International Conference on Computer Engineering and Applications - Volume 01

In this paper, we design a new kind of patterns, named high transaction-weighted utility itemsets, which considers not only individual profits and quantities of the items in a transaction, but also the contribution of each transaction in a database. We ...
Mining summarization of high utility itemsets

Mining interesting itemsets from transaction databases has attracted a lot of research interests for decades. In recent years, high utility itemset (HUI) has emerged as a hot topic in this field. In real applications, the bottleneck of HUI mining is not ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 13, Issue 1

March 2022

203 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3483343

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 October 2021

Accepted: 01 April 2021

Revised: 01 March 2021

Received: 01 November 2020

Published in TMIS Volume 13, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

24
Total Citations
View Citations
391
Total Downloads

Downloads (Last 12 months)67
Downloads (Last 6 weeks)13

Reflects downloads up to 20 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

K LS SS SS A(2024)High Utility Itemset Extraction using PSO with Online Control Parameter CalibrationInternational Journal of Next-Generation Computing10.47164/ijngc.v15i1.1643Online publication date: 14-May-2024
https://doi.org/10.47164/ijngc.v15i1.1643
Gao ZHan MLiu SLi AMu D(2024)High utility itemsets mining based on hybrid harris hawk optimization and beluga whale optimization algorithmsJournal of Intelligent & Fuzzy Systems10.3233/JIFS-23679346:4(7567-7602)Online publication date: 18-Apr-2024
https://doi.org/10.3233/JIFS-236793
Jiang HWu GSun MLi FSun YFang W(2024)PHUI-GA: GPU-Based Efficiency Evolutionary Algorithm for Mining High Utility ItemsetsJournal of Systems Engineering and Electronics10.23919/JSEE.2024.00002035:4(965-975)Online publication date: Aug-2024
https://doi.org/10.23919/JSEE.2024.000020
Wu YWang ZLi YGuo YJiang HZhu XWu X(2024)Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time SeriesACM Transactions on Management Information Systems10.1145/365845015:2(1-27)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3658450
Atmaja ESonawane K(2024)Modified Genetic Algorithm for Efficient High-Utility Itemset MiningInternational Journal of Computational Intelligence and Applications10.1142/S146902682450024XOnline publication date: 30-Sep-2024
https://doi.org/10.1142/S146902682450024X
Li YMa CGao RWu YLi JWang WWu X(2024)OPF-Miner: Order-Preserving Pattern Mining With Forgetting Mechanism for Time SeriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.343827436:12(8981-8995)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3438274
Lee CKim HCho MKim HVo BLin JFournier-Viger PYun U(2024)Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic DatabaseIEEE Access10.1109/ACCESS.2024.340656212(77605-77620)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3406562
Yan YNiu XZhang ZFournier-Viger PYe LMin F(2024)Efficient high utility itemset mining without the join operationInformation Sciences10.1016/j.ins.2024.121218681(121218)Online publication date: Oct-2024
https://doi.org/10.1016/j.ins.2024.121218
Song WSun ZFournier-Viger PWu Y(2024)MRI-CEInformation Sciences: an International Journal10.1016/j.ins.2024.120392665:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120392
Mofid ADaneshpour NTorabi ZTaghavi P(2024)TKU-BChOA: an accurate meta-heuristic method to mine Top-k high utility itemsetsThe Journal of Supercomputing10.1007/s11227-024-06275-780:14(21284-21305)Online publication date: 7-Jun-2024
https://dl.acm.org/doi/10.1007/s11227-024-06275-7
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents