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A Parameter-Free Method for Discovering Generalized Clusters in a Network

机译：一种无参数的网络中通用簇发现方法

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We show that an MDL-based graph clustering method may be used for discovering generalized clusters from a graph and then extend it so that the input is a network. We define intuitively that generalized clusters contain at least a cluster in which nodes are connected sparsely and the cluster is connected either densely to another cluster or sparsely to another conventional cluster. The first characteristic of the MDL-based graph clustering is a direct outcome of an entropy function used in measuring the encoding length of clusters and the second one is realized through our new encoding method. Experiments using synthetic and real data sets give promising results.

机译：我们展示了基于MDL的图聚类方法可用于从图中发现广义聚类，然后对其进行扩展，以使输入为网络。我们直观地定义，广义集群至少包含一个集群，其中节点稀疏连接，并且该集群密集地连接到另一个集群，或者稀疏地连接到另一个常规集群。基于MDL的图聚类的第一个特征是用于测量聚类的编码长度的熵函数的直接结果，而第二个特征是通过我们的新编码方法实现的。使用综合和真实数据集进行的实验给出了可喜的结果。

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来源
《Discovery science》|2011年|p.135-149|共15页
会议地点 Espoo(FI);Espoo(FI)
作者
Hiroshi Hirai; Bin-Hui Chou; Einoshin Suzuki;
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作者单位

Department of Informatics, ISEE, Kyushu University, Fukuoka 819-0395, Japan;

Department of Informatics, ISEE, Kyushu University, Fukuoka 819-0395, Japan;

Department of Informatics, ISEE, Kyushu University, Fukuoka 819-0395, Japan;

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正文语种 eng
中图分类人工智能理论;
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A Parameter-Free Method for Discovering Generalized Clusters in a Network

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