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arules - A Computational Environment for Mining Association Rules and Frequent Item Sets

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  • Hahsler, Michael
  • Grün, Bettina
  • Hornik, Kurt

Abstract

Mining frequent itemsets and association rules is a popular and well researched approach for discovering interesting relationships between variables in large databases. The R package arules presented in this paper provides a basic infrastructure for creating and manipulating input data sets and for analyzing the resulting itemsets and rules. The package also includes interfaces to two fast mining algorithms, the popular C implementations of Apriori and Eclat by Christian Borgelt. These algorithms can be used to mine frequent itemsets, maximal frequent itemsets, closed frequent itemsets and association rules.

Suggested Citation

  • Hahsler, Michael & Grün, Bettina & Hornik, Kurt, 2005. "arules - A Computational Environment for Mining Association Rules and Frequent Item Sets," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i15).
    • Handle: RePEc:jss:jstsof:v:014:i15
    DOI: http://hdl.handle.net/10.18637/jss.v014.i15
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    Cited by:

    1. Hofmarcher, Paul & Crespo Cuaresma, Jesus & Grün, Bettina & Humer, Stefan & Moser, Mathias, 2018. "Bivariate jointness measures in Bayesian Model Averaging: Solving the conundrum," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 150-165.
    2. Man-, ZuyiKeunZuyi Wang & Takagi, Chifumi & Kim, Man-Keun & Chung, Anh, 2022. "Uncover Drivers Influencing Consumers' WTP Using Machine Learning: Case of Organic Coffee in Taiwan," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322150, Agricultural and Applied Economics Association.
    3. Khanh Giang Le & Quang Hoc Tran & Van Manh Do, 2023. "Urban Traffic Accident Features Investigation to Improve Urban Transportation Infrastructure Sustainability by Integrating GIS and Data Mining Techniques," Sustainability, MDPI, vol. 16(1), pages 1-19, December.
    4. Jasleen Kaur & Khushdeep Dharni, 2022. "Assessing efficacy of association rules for predicting global stock indices," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 49(3), pages 329-339, September.
    5. Crespo Cuaresma, Jesus & Grün, Bettina & Hofmarcher, Paul & Humer, Stefan & Moser, Mathias, 2015. "A Comprehensive Approach to Posterior Jointness Analysis in Bayesian Model Averaging Applications," Department of Economics Working Paper Series 193, WU Vienna University of Economics and Business.
    6. Sun, Chenhao & Wang, Xin & Zheng, Yihui, 2020. "An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks," Applied Energy, Elsevier, vol. 258(C).
    7. Ji Yeon Lee & Richa Kumari & Jae Yun Jeong & Tae-Hyun Kim & Byeong-Hee Lee, 2020. "Knowledge Discovering on Graphene Green Technology by Text Mining in National R&D Projects in South Korea," Sustainability, MDPI, vol. 12(23), pages 1-16, November.
    8. Suelane Garcia Fontes & Ronaldo Gonçalves Morato & Silvio Luiz Stanzani & Pedro Luiz Pizzigatti Corrêa, 2021. "Jaguar movement behavior: using trajectories and association rule mining algorithms to unveil behavioral states and social interactions," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-18, February.
    9. Da-Yeong Lee & Dae-Seong Lee & Young-Seuk Park, 2022. "Taxonomic and Functional Diversity of Benthic Macroinvertebrate Assemblages in Reservoirs of South Korea," IJERPH, MDPI, vol. 20(1), pages 1-17, December.
    10. Mulenga, Brian P. & Raper, Kellie Curry & Peel, Derrell S., 2020. "A Market Basket Analysis of Beef Calf Management Practice Adoption," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 46(2), August.
    11. Deszczyński, Bartosz & Beręsewicz, Maciej, 2021. "The maturity of relationship management and firm performance – A step toward relationship management middle-range theory," Journal of Business Research, Elsevier, vol. 135(C), pages 358-372.
    12. Yoonju Lee & Heejin Kim & Hyesun Jeong & Yunhwan Noh, 2020. "Patterns of Multimorbidity in Adults: An Association Rules Analysis Using the Korea Health Panel," IJERPH, MDPI, vol. 17(8), pages 1-14, April.
    13. Yoichi Matsumoto, 2013. "Heterogeneous Combinations of Knowledge Elements: How the Knowledge Base Structure Impacts Knowledge-related Outcomes of a Firm," Discussion Paper Series DP2013-15, Research Institute for Economics & Business Administration, Kobe University.
    14. Scholz, Michael, 2016. "R Package clickstream: Analyzing Clickstream Data with Markov Chains," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i04).
    15. Kurt Hornik & Christian Buchta & Achim Zeileis, 2009. "Open-source machine learning: R meets Weka," Computational Statistics, Springer, vol. 24(2), pages 225-232, May.
    16. Małecka-Ziembińska Edyta & Siwiec Anna, 2020. "Searching for similarities in EU corporate income taxes for their harmonization," Economics and Business Review, Sciendo, vol. 6(4), pages 72-94, December.
    17. Nancy Awad & Jean-Francois Couchot & Bechara Al Bouna & Laurent Philippe, 2020. "Publishing Anonymized Set-Valued Data via Disassociation towards Analysis," Future Internet, MDPI, vol. 12(4), pages 1-21, April.
    18. Michael Hahsler & Radoslaw Karpienko, 2017. "Visualizing association rules in hierarchical groups," Journal of Business Economics, Springer, vol. 87(3), pages 317-335, April.

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