高维稀疏数据频繁项集挖掘算法的研究

传统挖掘算法不适用于挖掘高维稀疏数据集.提出了一种针对高维稀疏数据的频繁项集挖掘算法FIHS.FIHS引入了一种新的数据结构用来存储频繁项集,该结构不但可以减少存储空间,而且可以降低计数代价.该算法只需扫描一次数据集,通过优化连接剪枝操作避免产生非频繁的候选项集,基于K-频繁项集使用"与"、"或"操作产生K+1-频繁项...

Research on Frequent Itemsets Mining Algorithm Based on High-dimensional Sparse Dataset

The traditional mining algorithms arc not applicable to mine high-dimensional sparse dataset,a new frequent itemsets mining algorithm based on high-dimensional sparse dataset named FIRS (Frequent mining algorithm based on High-dimensional Sparse dataset) was proposed in this paper. FIHS adopts a new data structure to store frequcnt itemsets, using this structure can reduce the storage space and the cost of counting. FIHS can avoid generating infrectuent candidate itemsets through optimizing the operation of connection and pruning,which rectuires scan the dataset once. What's more,just by applying ANIX)R operation,frequcnt K+1-itemsets can be created according to frequent K-itemsets, and the maintenance of the data structure is simple. According to theoretical analysis and experiments, the improved algorithm enjoys many advantages aiming at high-dimensional sparse dataset, such as quick mining, less memory spacc,etc.