K-Medoids-Based Consensus Clustering Based on Cell-Like P Systems with Promoters and Inhibitors
K-Medoids-Based Consensus Clustering Based on Cell-Like P Systems with Promoters and Inhibitors
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DOI：
10.1007/978-981-10-3611-8_11
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Xiyu Liu;Yuzhen Zhao;Wenxing Sun
中科院分区：
文献类型：
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作者：
Xiyu Liu;Yuzhen Zhao;Wenxing Sun
一致性聚类是一类稳健的聚类算法，它是在已有的多个基本划分的基础上得到最终的聚类结果。本文将K-medoid算法和具有启动子和抑制子的类细胞P系统(一类并行分布式计算模型)引入到共识聚类中，提出了基于K-medoid算法的带有启动子和抑制子的类细胞P系统的共识聚类。通过实验，本文提出的共识聚类算法能够在较短的时间内获得高质量的聚类结果。本研究改进了TKDE，2015，2,155-169中的结果。
Consensus clustering is a class of robust clustering algorithms, which obtain the finally clustering results based on multiple existing basic partitionings. In this study, we introduce the K-medoids algorithm and the cell-like P systems with promoters and inhibiters (a class of parallel and distributed computing models) to the consensus clustering, and propose the K-medoids-based consensus clustering based on the cell-like P system with promoters and inhibiters. Through the experiment, the proposed consensus clustering algorithm can obtain high quality clustering results in a short time. This study improves the result inTKDE, 2015, 2, 155–169.