一种基于混合搜索的高效Top-K最频繁模式挖掘算法

挖掘数据集中的Top-K最频繁模式具有重要意义.已有Top-K最频繁模式挖掘算法通常采用最频繁的k个项目作为初始项目,并将初始项目中频率最低的项目的支持度作为初始边界支持度.但实际组成Top-K最频繁模式的项目数目可能远少于k,从而制约了算法的效率.为此,提出了一种基于混合搜索方式的高效Top-K最频繁模式挖掘算法MTKFP.该算法首先利用宽度优先搜索获得少量的短项集,并利用短项集确定数目少于k的初始项目范围以及较高的初始边界支持度;然后利用深度优先搜索获得所有Top-K最频繁模式.实验表明,MTKFP算法所获得的初始项目数目至少低于已有算法70%,初始边界支持度高于已有算法;NTKFP算法的性能优于已有最好算法.

An Efficient Mixed-searching-based Algorithm for Mining Top-K Most-frequent Patterns

It is significant to mine Top-K most-frequent patterns in dataset.The existing algorithms usually use the k-most frequent items as the initial items,and use the support of item with lowest frequency in initial items as the initial border support.In fact,since the number of items in Top-K most-frequent patterns is much less than k,the efficiency of the existing algorithms is restricted.To solve this problem,an efficient mixed-searching-based algorithm for mining Top-K most-frequent patterns,MTKFP is presente...