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Modified semi-supervised affinity propagation clustering with fuzzy density fruit fly optimization

  • S.I. : Higher Level Artificial Neural Network Based Intelligent Systems
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Abstract

Affinity propagation (AP) is a clustering method that takes as input measures of similarity between pairs of data points. As the oscillations and preference value need to be preset, the algorithm precision could not be controlled exactly. To improve the performance of AP, this study utilizes priori pairwise constraints to obtain the reliable similarity matrix named semi-supervised affinity propagation (SAP). To find the best solution in domain of preference value, this study also proposes an improved fruit fly optimization (IFO) to optimize the unknown parameters of the SAP model. The IFO algorithm has introduced the fuzzy density mechanism to enhance the searching capacities of fruit fly individuals. The benchmark functions experiments indicate that the IFO algorithm has better precision and convergence speed than other compared swarm intelligence algorithms. We used SAP that based on IFO to identify UCI datasets and synthetic datasets. The simulation results show that proposed clustering algorithm produces significantly better clustering quality and accuracy results. In addition, we utilized the improved model to analyse the seismic data. The clustering results indicated that the proposed model had the better research potential and the good application value.

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Acknowledgements

The work was supported by the National Science Foundation of China under Grant 61472049, 61572225 and 61202309; the key scientific research projects of colleges and universities of Henan Province (No. 21A520012), the Jilin province social science fund project (No. 2019B69), the 2018 Jilin province higher education teaching reform research project, and the 2018 Jilin university of finance and economics key project.

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Authors and Affiliations

  1. School of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun, China

    Ruihong Zhou & Limin Wang

  2. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Qiaoming Liu

  3. College of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou, China

    Jian Wang

  4. School of Computer Science and Engineering, Changchun University of Technology, Changchun, China

    Xuming Han

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  1. Ruihong Zhou
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  2. Qiaoming Liu
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  4. Xuming Han
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  5. Limin Wang
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Correspondence to Qiaoming Liu.

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Ruihong Zhou and Qiaoming Liu have contributed equally to this work.

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Zhou, R., Liu, Q., Wang, J. et al. Modified semi-supervised affinity propagation clustering with fuzzy density fruit fly optimization. Neural Comput & Applic 33, 4695–4712 (2021). https://doi.org/10.1007/s00521-020-05431-3

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