An algorithm for mining frequent subgraphs based on associated matrix was proposed.
提出了一种基于关联矩阵的频繁子图挖掘算法。
Therefore it is imperative to improve the efficiency of mining the frequent subgraphs.
随着图的广泛应用，图的规模不断扩大，因此提高频繁子图挖掘效率势在必行。
To solve the above two problems, authors propose a new depth-first algorithm to discover weighted maximal frequent subgraphs only.
为解决上述问题，提出了一种深度优先的挖掘加权最大频繁子图的新算法。
Secondly, we processed the edge expansion and frequent sub-graph isomorphism which are the most complexity parts of frequent subgraphs mining in parallel.
其次，将频繁子图边扩展及同构判断这部分频繁子图挖掘算法中时间复杂度最高的部分并行处理。
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。
Aiming at this problem, graph based link discovery technology, which compresses the fund exchange graph by frequent subgraphs, can detect the criminal pattern of groups with illegal fund exchanges.
针对这一问题，采用基于图的链接发现技术，利用频繁子图对资金交易图进行压缩，可以有效地侦测群体的异常资金交易结构。
Aiming at this problem, graph based link discovery technology, which compresses the fund exchange graph by frequent subgraphs, can detect the criminal pattern of groups with illegal fund exchanges.
针对这一问题，采用基于图的链接发现技术，利用频繁子图对资金交易图进行压缩，可以有效地侦测群体的异常资金交易结构。
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