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DBSCAN-based granular descriptors for rule-based modeling

  • Foundation, algebraic, and analytical methods in soft computing
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Abstract

Rule-based modeling is a useful approach in modeling both complex nonlinear and non-numeric systems, e.g. having linguistic information. However, modeling complex systems in big data era brings new challenges for conventional rule-based modeling, such as high computation overhead and low representation ability of rule. To address these problems, this paper proposed an advanced rule-based modeling method-based DBSCAN-inspired granular descriptors. First, to understand the essential characteristics of data and enhance rules’ representation ability, data structures are obtained by DBSCAN clustering algorithm, which has high flexibility at coping with diverse geometry. Second, numerous granular descriptors are constructed in the refined representation of data structures and used for fuzzy rule formation. This granular computing process could effectively reduce computation overhead of big data analysis. Finally, the proposed rule-based model consists of fuzzy rules and interval outputs, which are resulted from structural granular descriptors and justifiable granulating respectively. Experimental studies concerning synthetic data and publicly available data illustrated that the proposed method can achieve prior performance on both modeling and time consuming than conventional rule-based modeling via FCM. Therefore, it is verified the developed approach is feasible and useful to be applied in modeling real complex engineering systems.

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Data availability

The data that support the findings of this study are available from UCI website (https://archive.ics.uci.edu/ml/datasets.php)

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Acknowledgements

This research is support by JSPS Grant-in-Aid for Early-Career Scientists (Grant Number 22K17961).

Funding

This research is supported by JSPS KAKENHI Grant JP22K17961.

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Authors and Affiliations

  1. Digital Architecture Research Center (DigiARC), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan

    Tinghui Ouyang

  2. Nihon University, Tokyo, Japan

    Xinhui Zhang

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  1. Tinghui Ouyang
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  2. Xinhui Zhang
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Correspondence to Tinghui Ouyang.

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Ouyang, T., Zhang, X. DBSCAN-based granular descriptors for rule-based modeling. Soft Comput 26, 13249–13262 (2022). https://doi.org/10.1007/s00500-022-07514-w

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