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An improved MkNN clustering algorithm based on graph theory and membrane computing

机译:基于曲线图理论和膜计算的改进的MKNN聚类算法

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This paper combines the graph theory and P system to solve the clustering problem. In order to effectively identify clusters with arbitrary shapes and uneven densities, we combine MkNN clustering algorithm and graph theory to propose a mutual k -nearest neighbors graph (MkNNG) clustering algorithm. In order to further improve the efficiency of MkNNG algorithm, based on the non-determinism and great parallelism of P system, a cell-like P system with multi-promoters and multi-inhibitors named mutual k -nearest neighbors graph P system (MkNNG-P) is designed. And then based on MkNNG-P system, a novel clustering algorithm named MkNNG-P clustering algorithm is proposed, which uses the membrane objects and rules to solve the clustering problem. MkNNG-P algorithm first calculates the dissimilarity between any two nodes in n - 1 membranes in parallel. After then it uses one membrane to get k -nearest neighbors of n nodes. Finally, one membrane is used to find mutual k -nearest neighbors and construct MkNNG to discover the natural clusters in the data set. Experiments show that MkNNG-P algorithm has the advantages of both MkNNG and P system. It not only can obtain good clustering quality for data of different sizes and shapes without presetting clustering numbers, but also has extremely high computing speed.
机译:本文结合了图形理论和P系统来解决聚类问题。为了有效地识别具有任意形状和不均匀密度的簇,我们将MKNN聚类算法和图解理论组合起来提出了一种相互K-Nearest邻居图(MKNng)聚类算法。为了进一步提高Mknng算法的效率,基于P系统的非确定性和巨大并行性,一种具有多启动子和多抑制剂的细胞样P系统,名为MKS-Nearest邻居图P系统(MKNNG- p)设计。然后基于MKNNG-P系统,提出了一种名为MKNNG-P集群算法的新型聚类算法,其使用膜对象和规则来解决聚类问题。 MKNNG-P算法首先并行计算N - 1膜中的任何两个节点之间的异化性。然后,它使用一个膜来获得n节点的k-nealest邻居。最后,一个膜用于找到相互K -Nealest邻居,并构建Mknng以发现数据集中的自然集群。实验表明,MKNNG-P算法具有MKNNG和P系统的优点。它不仅可以为不同尺寸和形状的数据获得良好的聚类质量而不预设聚类数字,但也具有极高的计算速度。

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