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Mining fuzzy association rules from uncertain data

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Abstract

Association rule mining is an important data analysis method that can discover associations within data. There are numerous previous studies that focus on finding fuzzy association rules from precise and certain data. Unfortunately, real-world data tends to be uncertain due to human errors, instrument errors, recording errors, and so on. Therefore, a question arising immediately is how we can mine fuzzy association rules from uncertain data. To this end, this paper proposes a representation scheme to represent uncertain data. This representation is based on possibility distributions because the possibility theory establishes a close connection between the concepts of similarity and uncertainty, providing an excellent framework for handling uncertain data. Then, we develop an algorithm to mine fuzzy association rules from uncertain data represented by possibility distributions. Experimental results from the survey data show that the proposed approach can discover interesting and valuable patterns with high certainty.

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Authors and Affiliations

  1. Department of Management Information Systems, Central Taiwan University of Science and Technology, Taichung, 406, Taiwan, ROC

    Cheng-Hsiung Weng

  2. Department of Information Management, National Central University, Chung-Li, 320, Taiwan, ROC

    Yen-Liang Chen

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  1. Cheng-Hsiung Weng
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  2. Yen-Liang Chen
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Correspondence to Cheng-Hsiung Weng.

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Weng, CH., Chen, YL. Mining fuzzy association rules from uncertain data. Knowl Inf Syst 23, 129–152 (2010). https://doi.org/10.1007/s10115-009-0223-1

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