微阵列数据中Top-k频繁闭合项集挖掘

摘要

Most previous mining frequent closed item sets require the specification of a minimum support threshold in microarry data. However, it is difficult for users to provide an appropriate minimum support threshold in practice. Aiming at this problem, this paper presents a top-k frequent closed item set and an algorithm in microarray data. The algorithm uses top-down breadth-first search strategy to mining top-k frequent closed item set of length no less than given value min_l and pruning the search space effectively to improve the search speed. Experimental result shows that the time performance of this algorithm outperforms the CARPENTER algorithm in most cases.%现有大部分微阵列数据中频繁闭合项集的挖掘需要事先给定最小支持度,但在实际应用中该最小支持度很难确定.针对该问题,提出top-k频繁闭合项集挖掘算法,基于自顶向下宽度优先搜索策略挖掘项集长度不小于min_1的top-k频繁闭合项集,并对搜索空间进行有效修剪,从而提高搜索速度.实验结果表明,该算法的时间性能在多数情况F优于CARPENTER算法.