基于Top-K项频繁模式挖掘的研究及实现

Research and Implementation of Top-k Frequent Patterns Mining

频繁模式挖掘是关联规则、序列分析等数据挖掘任务的关键步骤,我们知道,当给定的最小支持度阈值非常小,将产生大量的频繁模式,反之,可能产生很少的模式或根本没有结果。用户有时仅对其中的部分项的频繁度感兴趣,这属于部分频繁模式挖掘问题。文章通过有效设置挖掘区间,讨论一种top-k项频繁模式挖掘问题,进而扩展到连续区间上的情况,最后将给出实验结果。

Frequent pattern mining is a key step for many tasks of data mining such as association rules mining, series analysis and so on. As we know, when the minimum support threshold is low, we may acquire large numbers of frequent patterns, Whereas we may get few or even no frequent pattern. Sometimes users are only interested in frequency about part of all items, which falls into the problem of mining part of frequent patterns. In this paper, we discuss a kind of top k frequent patterns mining and then extend it to the situation in a sequential range. Finally, we present our experiment result.