解决数据样本不平衡性的频繁子图挖掘算法

Algorithm considering imbalance across datasets for mining frequent subgraphs

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。

Traditional algorithms for frequent subgraphs mining have limits when dealing with biological datasets.Biological network 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 interaction networks,it is proved that the algorithm is feasible.