基于COFI-Tree的N-最有兴趣项目集挖掘算法

BOMO算法采用递归构造条件子树,在挖掘大数据集时耗时较长,执行效率低,为了解决这一不足,文中给出一种基于COFI-Tree的挖掘N-最有兴趣项目集算法。算法采用COFI-Tree结构,无需递归构造条件子树FP-Tree,在同一时间内只有一个COFI-Tree在内存,并且有效地减少了其运算时间。通过对两种算法进行对比分析,实验结果得出：该算法比BOMO算法程序执行时间明显缩短;在挖掘大数据集时执行效率显著提高,尤其是k〈4时,性能最好。由此可见,改进后的算法是可行有效的。

Mining N-Most Interesting Itemsets Algorithm Based on COFI-Tree

BOMO algorithm constructs conditional FP-Tree recursively so that it requires more memory and CPU resources.To solve this problem,an algorithm for mining N-most interesting itemsets based on COFI-Tree is presented.This algorithm adopts COFI-Tree.COFI-Tree doesn＇t need to construct conditional FP-Tree recursively and there is only one COFI-Tree in memory at a time.Experiment shows that the algorithm based on COFI-Tree performs faster than current best algorithm BOMO;The algorithm has good performance for large data set,especially it shows the best when for k value is smaller than 4.This shows that the improved algorithm is feasible and effective.