解决数据样本不平衡性的频繁子图挖掘算法

传统的图挖掘算法应用到生物数据上有其局限性。根据生物网络的特性，通过引入相对支持度的概念，提出了一种解决数据样本间不平衡性的频繁子图挖掘算法——IFS算法。通过对真实的蛋白质互作网络进行处理，证明该算法是可行的。

Algorithm considering imbalance across datasets for mining frequent subgraphs

Traditional algorithms for frequent subgraphs mining have limits when dealing with biological datasets.Biological net- work has its own characters.Based on these characters,authors propose a new algorithm considering imbalance across datasets, called IFS(Iterated Function System),for mining frequent subgaraphs by relative support.Through dealing with the real protein in- teraction networks,it is proved that the algorithm is feasible.