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Fuzzy Connected-Triple for Predicting Inter-variable Correlation

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Advances in Computational Intelligence Systems (UKCI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 650))

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Abstract

Identifying relationship between attribute variables from different data sources is an emerging field in data mining. However, currently there seldom exist effective methods designed for this particular problem. In this paper, a novel approach for inter-variable correlation prediction is proposed through the employment of the concept of connected-triple, and implemented with fuzzy logic. By the use of link strength measurements and fuzzy inference, the job of detecting similar or related variables can be accomplished via examining the link relation patterns. Comparative experimental investigations are carried out, demonstrating the potential of the proposed work in generating acceptable predicted results, while involving only simple computations.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Institute of Mathematics, Physics and Computer Science, Aberystwyth University, Aberystwyth, UK

    Zhenpeng Li, Changjing Shang & Qiang Shen

Authors
  1. Zhenpeng Li
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  2. Changjing Shang
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  3. Qiang Shen
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Corresponding author

Correspondence to Changjing Shang .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen Shi, Fujian, China

    Fei Chao

  2. Cardiff University, Cardiff, United Kingdom

    Steven Schockaert

  3. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong

    Qingfu Zhang

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Li, Z., Shang, C., Shen, Q. (2018). Fuzzy Connected-Triple for Predicting Inter-variable Correlation. In: Chao, F., Schockaert, S., Zhang, Q. (eds) Advances in Computational Intelligence Systems. UKCI 2017. Advances in Intelligent Systems and Computing, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-66939-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-66939-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66938-0

  • Online ISBN: 978-3-319-66939-7

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