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中文摘要：
为了解决大规模图集挖掘算法PartGraphMining必须重复扫描图集才能得到全部频繁子图的缺点，提出了一种改进的IPMC算法，通过hash表保存同构图的hash地址和支持度，不必重复扫描图集就可快速得到釜部频繁子图，再经过少量的子图同构判断得到全部频繁闭图。在实际数据集上运行的实验结果表明它比原算法的挖掘效率有所提高。
英文摘要：
In order to solve the shortage of the PartGraphMining algorithm for mining large-scale graph databases must repeatedly scan the database that could get all frequent subgraph patterns, this paper proposed a new algorithm IPMC. It could get all frequent subgrapb patterns quickly without scanning the database repeatedly through storing graph＇ s hash address and supportting in the hash table. Furthermore, obtained all closed frequent graph patterns by the judgement of few subgraph isomorphism. The experimental result on real datasets shows that new algorithm improves the efficiency of mining.
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