文章基本信息

标题：Research of Improved FP-Growth Algorithm in Association Rules Mining
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作者：Yi Zeng ; Shiqun Yin ; Jiangyue Liu 等
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2015
卷号：2015
DOI：10.1155/2015/910281
出版社：Hindawi Publishing Corporation
摘要：Association rules mining is an important technology in data mining. FP-Growth (frequent-pattern growth) algorithm is a classical algorithm in association rules mining. But the FP-Growth algorithm in mining needs two times to scan database, which reduces the efficiency of algorithm. Through the study of association rules mining and FP-Growth algorithm, we worked out improved algorithms of FP-Growth algorithm—Painting-Growth algorithm and N (not) Painting-Growth algorithm (removes the painting steps, and uses another way to achieve). We compared two kinds of improved algorithms with FP-Growth algorithm. Experimental results show that Painting-Growth algorithm is more than 1050 and N Painting-Growth algorithm is less than 10000 in data volume; the performance of the two kinds of improved algorithms is better than that of FP-Growth algorithm.