IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v9y2022i4d10.1007_s40745-021-00345-w.html
   My bibliography  Save this article

A Novel Association Rule Mining Method for Streaming Temporal Data

Author

Listed:
  • Hui Zheng

    (Nanjing University of Posts and Telecommunications
    Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks
    Swinburne University of Technology)

  • Peng LI

    (Nanjing University of Posts and Telecommunications
    Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks)

  • Jing HE

    (Swinburne University of Technology)

Abstract

Streaming temporal data contains time stamps and values, challenging to quantify relationships of time stamps and corresponding values. Moreover, the characteristics and relationships of streaming temporal data are not invariable. Thus, it is impossible to analyse all data by a trained model at the beginning of data streams. Practically, the trained model to analyse streaming temporal data should change according to the increasing volume of data. Association rule mining, on the other hand, can find potential relationships from given data. This paper proposes an association rule mining method for streaming temporal data to discover potential relationships from streaming temporal data. Our experiments verify our proposed method. A public data set is applied to compare the performance of the proposed method and its counterpart. A small data set is also applied for two case studies to further illustrate our proposed method mine association rules with streaming temporal data with time stamps and corresponding values.

Suggested Citation

  • Hui Zheng & Peng LI & Jing HE, 2022. "A Novel Association Rule Mining Method for Streaming Temporal Data," Annals of Data Science, Springer, vol. 9(4), pages 863-883, August.
    • Handle: RePEc:spr:aodasc:v:9:y:2022:i:4:d:10.1007_s40745-021-00345-w
    DOI: 10.1007/s40745-021-00345-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-021-00345-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-021-00345-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pat Langley & Jaime G. Carbonell, 1984. "Approaches to machine learning," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 35(5), pages 306-316, September.
    2. Hossein Hassani & Xu Huang & Mansi Ghodsi, 2018. "Big Data and Causality," Annals of Data Science, Springer, vol. 5(2), pages 133-156, June.
    3. Markku Ollikainen & Erkki Koskela, 2001. "Optimal Private and Public Harvesting under Spatial and Temporal Interdependence," CESifo Working Paper Series 452, CESifo.
    4. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Heba Soltan Mohamed & M. Masoom Ali & Haitham M. Yousof, 2023. "The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance," Annals of Data Science, Springer, vol. 10(5), pages 1199-1216, October.
    2. Roberto Moro-Visconti & Salvador Cruz Rambaud & Joaquín López Pascual, 2023. "Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    3. Mansoureh Beheshti Nejad & Seyed Mahmoud Zanjirchi & Seyed Mojtaba Hosseini Bamakan & Negar Jalilian, 2024. "Blockchain Adoption in Operations Management: A Systematic Literature Review of 14 Years of Research," Annals of Data Science, Springer, vol. 11(4), pages 1361-1389, August.
    4. M. Sridharan, 2023. "Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters," Annals of Data Science, Springer, vol. 10(4), pages 1107-1125, August.
    5. Amaal Elsayed Mubarak & Ehab Mohamed Almetwally, 2024. "Modelling and Forecasting of Covid-19 Using Periodical ARIMA Models," Annals of Data Science, Springer, vol. 11(4), pages 1483-1502, August.
    6. Xueyan Xu & Fusheng Yu & Runjun Wan, 2023. "A Determining Degree-Based Method for Classification Problems with Interval-Valued Attributes," Annals of Data Science, Springer, vol. 10(2), pages 393-413, April.
    7. Qinghua Zheng & Chutong Yang & Haijun Yang & Jianhe Zhou, 2020. "A Fast Exact Algorithm for Deployment of Sensor Nodes for Internet of Things," Information Systems Frontiers, Springer, vol. 22(4), pages 829-842, August.
    8. Prashant Singh & Prashant Verma & Nikhil Singh, 2022. "Offline Signature Verification: An Application of GLCM Features in Machine Learning," Annals of Data Science, Springer, vol. 9(6), pages 1309-1321, December.
    9. Shah Hussain & Muhammad Qasim Khan, 2023. "Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning," Annals of Data Science, Springer, vol. 10(3), pages 637-655, June.
    10. A. R. Sherwani & Q. M. Ali, 2023. "Parametric Classification using Fuzzy Approach for Handling the Problem of Mixed Pixels in Ground Truth Data for a Satellite Image," Annals of Data Science, Springer, vol. 10(6), pages 1459-1472, December.
    11. Rakhal Das & Anjan Mukherjee & Binod Chandra Tripathy, 2022. "Application of Neutrosophic Similarity Measures in Covid-19," Annals of Data Science, Springer, vol. 9(1), pages 55-70, February.
    12. Rauscher, Michael & Barbier, Edward B., 2010. "Biodiversity and geography," Resource and Energy Economics, Elsevier, vol. 32(2), pages 241-260, April.
    13. Desmond Chekwube Bartholomew & Chrysogonus Chinagorom Nwaigwe & Ukamaka Cynthia Orumie & Godwin Onyeka Nwafor, 2024. "Intervention Analysis of COVID-19 Vaccination in Nigeria: The Naive Solution Versus Interrupted Time Series," Annals of Data Science, Springer, vol. 11(5), pages 1609-1634, October.
    14. Mäntymaa, Erkki & Juutinen, Artti & Tyrväinen, Liisa & Karhu, Jouni & Kurttila, Mikko, 2018. "Participation and compensation claims in voluntary forest landscape conservation: The case of the Ruka-Kuusamo tourism area, Finland," Journal of Forest Economics, Elsevier, vol. 33(C), pages 14-24.
    15. Morag F. Macpherson & Adam Kleczkowski & John Healey & Nick Hanley, 2015. "When to harvest? The effect of disease on optimal forest rotation," Discussion Papers in Environment and Development Economics 2015-19, University of St. Andrews, School of Geography and Sustainable Development.
    16. Muhammad Tahir & Sufyan Ali & Ayesha Sohail & Ying Zhang & Xiaohua Jin, 2024. "Unlocking Online Insights: LSTM Exploration and Transfer Learning Prospects," Annals of Data Science, Springer, vol. 11(4), pages 1421-1434, August.
    17. Newman, D.H., 2002. "Forestry's golden rule and the development of the optimal forest rotation literature," Journal of Forest Economics, Elsevier, vol. 8(1), pages 5-27.
    18. Junjie Hou & Yuqi Zhang & Duo Su, 2024. "Unified Image Harmonization with Region Augmented Attention Normalization," Annals of Data Science, Springer, vol. 11(5), pages 1865-1886, October.
    19. Muhammed Navas Thorakkattle & Shazia Farhin & Athar Ali khan, 2022. "Forecasting the Trends of Covid-19 and Causal Impact of Vaccines Using Bayesian Structural time Series and ARIMA," Annals of Data Science, Springer, vol. 9(5), pages 1025-1047, October.
    20. Bo Li & Guangle Du, 2024. "Reaction Function for Financial Market Reacting to Events or Information," Annals of Data Science, Springer, vol. 11(4), pages 1265-1290, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aodasc:v:9:y:2022:i:4:d:10.1007_s40745-021-00345-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.