Closed and Maximal Subgraph Mining in Internally and Externally Weighted Graph Databases
Closed and Maximal Subgraph Mining in Internally and Externally Weighted Graph Databases
复制标题
DOI：
10.1109/waina.2011.48
复制
发表时间：
2011-03
期刊：
影响因子：
--
通讯作者：
Tomonobu Ozaki;M. Etoh
中科院分区：
文献类型：
--
作者：
Tomonobu Ozaki;M. Etoh
我们在内部和外部加权的图形数据库中形式化了一个封闭和最大模式发现问题。我们引入了两个权重，内权和外权，它们分别表示图中每条边的效用和重要性，以及图本身的重要性和可靠性。在我们的公式中，具有两组权重的图精确地描述了要挖掘的目标数据。作为对传统子图挖掘算法的扩展，我们提出了一种挖掘算法wgMiner，用于发现加权图数据库中的所有闭合模式和最大模式。通过wgMiner实验，验证了该方法在通信网络模式挖掘中的有效性。
We formalize a problem of closed and maximal pattern discovery in internally and externally weighted graph databases. We introduce two weights, internal weights and external weights, which represent utility and significance of each edge in the graph, and importance and reliability of the graph itself, respectively. In our formulation, graphs with the two sets of weights describe the target data to be mined precisely. As an extension of traditional sub graph miners, we develop a mining algorithm called "wgMiner" for discovering all closed and maximal patterns in the weighted graph databases. With wgMiner, experiments demonstrate the effectiveness of our formulation in pattern mining from communication networks.