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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. 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.
    2. Jesus Crespo Cuaresma & Bettina Grün & Paul Hofmarcher & Stefan Humer & Mathias Moser, 2015. "A Comprehensive Approach to Posterior Jointness Analysis in Bayesian Model Averaging Applications," Department of Economics Working Papers wuwp193, Vienna University of Economics and Business, Department of Economics.
    3. 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.
    4. Scholz, Michael, 2016. "R Package clickstream: Analyzing Clickstream Data with Markov Chains," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i04).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    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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