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There are many algorithms for solving complex problems in supervised manner. However, unsupervised tasks are more common in real scenarios. Inspired by the idea of granular computing and the characteristics of human cognitive process, this paper proposes a complex tasks decomposition mechanism based on Density Peaks Clustering (DPC) to address complex tasks with an unsupervised process, which simulates the multi-granular observation and analysis of human being. Firstly, the DPC algorithm is modified to nullify its essential defects such as the difficulty of locating correct clustering centers and classifying them accurately. Then, the improved DPC algorithm is used to construct the initial decomposition solving space with multi-granularity theory. We also define subtask centers set and the granulation rules to guide the multi-granularity decomposing procedure. These rules are further used to decompose the solving space from coarse granules to the optimal fine granules with a convergent and automated process. Furthermore, comprehensive experiments are presented to verify the applicability and veracity of our proposed method in community-detection tasks with several benchmark complex social networks. The results show that our method outperforms other four state-of-the-art approaches.


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A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density Peaks

Show Author's information Ziling PangGuoyin Wang( )Jie Yang
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Post and Telecommunication, Chongqing 400060, China.

Abstract

There are many algorithms for solving complex problems in supervised manner. However, unsupervised tasks are more common in real scenarios. Inspired by the idea of granular computing and the characteristics of human cognitive process, this paper proposes a complex tasks decomposition mechanism based on Density Peaks Clustering (DPC) to address complex tasks with an unsupervised process, which simulates the multi-granular observation and analysis of human being. Firstly, the DPC algorithm is modified to nullify its essential defects such as the difficulty of locating correct clustering centers and classifying them accurately. Then, the improved DPC algorithm is used to construct the initial decomposition solving space with multi-granularity theory. We also define subtask centers set and the granulation rules to guide the multi-granularity decomposing procedure. These rules are further used to decompose the solving space from coarse granules to the optimal fine granules with a convergent and automated process. Furthermore, comprehensive experiments are presented to verify the applicability and veracity of our proposed method in community-detection tasks with several benchmark complex social networks. The results show that our method outperforms other four state-of-the-art approaches.

Keywords:

multi-granularity, task decomposition, density peaks, complex network
Received: 08 September 2017 Accepted: 30 March 2018 Published: 24 May 2018 Issue date: September 2018
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Publication history

Received: 08 September 2017
Accepted: 30 March 2018
Published: 24 May 2018
Issue date: September 2018

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© The author(s) 2018

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61572091) and Chongqing Postgraduate Scientific Research and Innovation Project (No. CYB16106).

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