摘要
FP-growth算法是不产生候选集的频集挖掘算法,对其分析和实现有重要意义。通过描述和分析FP-growth算法,利用C++STL对其进行了实现,最后在若干数据集上与Apriori算法进行了性能比较,获得了较Apriori算法更好的运算结果。
FP-growth is the algorithm mining frequent itemsets without generating candidate itemsets, and it is important to analyze and implement it. This paper first analyzes this algorithm, then discusses the implementation of the algorithm based on C＋＋ STL . In the end, the efficency of FP-growth has been compared with Apriori in some datasets, the better performance has been got.
出处
《广西工学院学报》
CAS
2005年第3期64-67,共4页
Journal of Guangxi University of Technology
