With the increasing demand of massive structured data analysis, mining frequent subgraph patterns from graph datasets has been an attention-deserving field.
随着对大量结构化数据分析需求的增长，从图集合中挖掘频繁子图模式已经成为数据挖掘领域的研究热点。
In the paper, based on hyper graph theory, a hypergraph model is proposed, which is useful for spatial data mining.
该文基于超图理论提出了超图模型并将其用于空间数据挖掘。
The surge of social network analysis also makes graph (network) data management become one of the hottest research topic within the database and data mining community.
社会网络分析的兴起也使得图(网络)数据管理成为了当前数据库和数据挖掘领域的一个研究热点。
An alternate way to solve these problems is to represent the transactions of those domains by graph, and find the frequent subgraphs by using graph-based data mining techniques.
一种解决的方法就是用图的形式表示这些领域的事务，然后利用基于图论的数据挖掘技术发现频繁子图。
Frequent patterns mining is an important aspect of data mining and includes mining transaction, sequence, tree and graph.
频繁模式挖掘是数据挖掘领域的一个重要方面，研究内容一般包括事务、序列、树和图。
We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis.
我们介绍了一种新的概念化框架，语义图像挖掘，使得研究者能够在网络数据分析中将图像挖掘和本体论推论结合起来。
We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis.
我们介绍了一种新的概念化框架，语义图像挖掘，使得研究者能够在网络数据分析中将图像挖掘和本体论推论结合起来。
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