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Efficient method for updating class association rules in dynamic datasets with record deletion

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Abstract

Association rule mining is an important topic in data mining. The problem is to discover all (or almost all) associations among items in the transaction database that satisfy some user-specified constraints. Usually, the constraints are related to minimal support and minimal confidence. Class association rules (CARs) are a special type of association rules that can be applied for classification problem. Previous research showed that classification based on association rules has higher accuracy than can be achieved with an inductive learning algorithm or C4.5. As such, many methods have been proposed for mining CARs, although these use batch processing. However, datasets are often changed, with records added or/and deleted, and consequently updating CARs is a challenging problem. This paper proposes an efficient method for updating CARs when records are deleted. First, we use an MECR-tree to store nodes for the original dataset. The information in the nodes of this tree are updated based on the deleted records. Second, the concept of pre-large itemsets is used to avoid rescanning the original dataset. Finally, we propose an algorithm to efficiently update and generate CARs. We also analyze the time complexity to show the efficiency of our proposed algorithm. The experimental results show that the proposed method outperforms mining CARs from the dataset after record deletion.

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Acknowledgments

This work was carried out during the tenure of an ERCIM ‘Alain Bensoussan’ Fellowship Programme.

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Authors and Affiliations

  1. Division of Knowledge and System Engineering for ICT, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Loan T. T. Nguyen

  2. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Loan T. T. Nguyen

  3. Knowledge Management Systems Division, Wroclaw University of Science and Technology, Wroclaw, Poland

    Ngoc-Thanh Nguyen

  4. Faculty of Information Technology, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Bay Vo

  5. Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland

    Hung Son Nguyen

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  1. Loan T. T. Nguyen
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  2. Ngoc-Thanh Nguyen
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  3. Bay Vo
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  4. Hung Son Nguyen
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Correspondence to Ngoc-Thanh Nguyen.

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Nguyen, L.T.T., Nguyen, NT., Vo, B. et al. Efficient method for updating class association rules in dynamic datasets with record deletion. Appl Intell 48, 1491–1505 (2018). https://doi.org/10.1007/s10489-017-1023-z

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  • DOI: https://doi.org/10.1007/s10489-017-1023-z

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