欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
提出一种称为TRAVEL的网络聚类算法．它能够产生包含所有可能密度聚类的网络链接遍历序列，并从中自动发现网络的全局优化聚类．然后，遍历序列被转换为连续子区间堆结构．在此基础上，提出一种聚类算法HCLU，可以无须用户干预地从连续子区间堆中自动发现网络的层次聚类边界．在真实网络以及计算机生成的仿真网络数据集上的实验结果表明，所提出的算法比目前的基准方法具有更高的聚类精度．此外，算法能够从各种带有噪声的网络中发现无冗余且鲁棒的层次社团结构．
英文摘要：
This paper proposes a density-based network clustering algorithm, TRAVEL. The algorithm produces a traveling order containing clustering with various densities and finds the optimal clusters in it. The traveling order is subsequently transformed into a data structure of contiguous subinterval heap based on which a clustering algorithm, HCLU, is designed to find the hierarchical cluster boundaries of the network without any user interaction. Experimental results on real-world and computer-generated synthetic networks show that the clustering accuracy of the proposed algorithms is higher than the baseline methods. Furthermore, they are able to produce robust hierarchical communities in various networks with low redundancy in the presence of noise.
同期刊论文项目
同项目期刊论文
A Computational Method Based on the Integration of Heterogeneous Networks for Predicting Disease-Gen
Seed selection strategy in global network alignment without destroying the entire structures of func
Predicting protein complexes in protein interaction networks using a core-attachment algorithm based
A degree-distribution based hierarchical agglomerative clustering algorithm for protein complexes id
Identification of overlapping and non-overlapping community structure by fuzzy clustering in complex
Scaling the Kernel Function based on the Separating Region in Input Space: A Data-dependent Way to I
Non-traditional spectral clustering algorithms for the detection of community structure in complex n
A network based method for analysis of lncRNA-disease associations and prediction of lncRNAs implica
期刊信息
位置：