A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density Peaks
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...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2018-09-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2018.9020023 |
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| author | Ziling Pang Guoyin Wang Jie Yang |
| author_facet | Ziling Pang Guoyin Wang Jie Yang |
| author_sort | Ziling Pang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-55652d32a54c456babb8c777743623ad |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2018-09-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-55652d32a54c456babb8c777743623ad2025-02-02T06:00:35ZengTsinghua University PressBig Data Mining and Analytics2096-06542018-09-011324525610.26599/BDMA.2018.9020023A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density PeaksZiling Pang0Guoyin Wang1Jie Yang2<institution content-type="dept">Chongqing Key Laboratory of Computational Intelligence</institution>, <institution>Chongqing University of Post and Telecommunication</institution>, <city>Chongqing</city> <postal-code>400060</postal-code>, <country>China</country>.<institution content-type="dept">Chongqing Key Laboratory of Computational Intelligence</institution>, <institution>Chongqing University of Post and Telecommunication</institution>, <city>Chongqing</city> <postal-code>400060</postal-code>, <country>China</country>.<institution content-type="dept">Chongqing Key Laboratory of Computational Intelligence</institution>, <institution>Chongqing University of Post and Telecommunication</institution>, <city>Chongqing</city> <postal-code>400060</postal-code>, <country>China</country>.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.https://www.sciopen.com/article/10.26599/BDMA.2018.9020023multi-granularitytask decompositiondensity peakscomplex network |
| spellingShingle | Ziling Pang Guoyin Wang Jie Yang A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density Peaks Big Data Mining and Analytics multi-granularity task decomposition density peaks complex network |
| title | A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density Peaks |
| title_full | A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density Peaks |
| title_fullStr | A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density Peaks |
| title_full_unstemmed | A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density Peaks |
| title_short | A Multi-granularity Decomposition Mechanism of Complex Tasks Based on Density Peaks |
| title_sort | multi granularity decomposition mechanism of complex tasks based on density peaks |
| topic | multi-granularity task decomposition density peaks complex network |
| url | https://www.sciopen.com/article/10.26599/BDMA.2018.9020023 |
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