成都理工大学学报（自然科学版）Issue(1)：110-114,5.DOI:10.3969/j.issn.1671-9727.2015.01.14
基于划分和压缩数据库的改进Apriori算法
Improved Apriori algorithm based on classification and database compression
摘要
Abstract
When the Apriori algorithm faces massive data,its rate is low.To counter the above problem,this paper puts forward an improved method based on the classification and database compression.Firstly,according to the appearing frequency of characteristic data,this method stores the data in a temporary array in ascending order.Then the original transaction database is divided into several disjoint transaction database in order to accommodate the daughter database in the memory.At last,the entire database frequent itemsets are calculated by the frequent itemsets calculated according to each daughter database, thereby eliminating the unnecessary redundant data. Through the improvement,the large data sets can be effectively divided and compressed,and the association rules can be tapped on the daughter database.The experimental results show that the improved Apriori algorithm has improved a lot in the speed and efficiency of mining the massive data.关键词
数据挖掘/关联规则/压缩数据库Key words
data mining/association rule/database compression分类
信息技术与安全科学引用本文复制引用
胡绿慧,任玉兰,何振林..基于划分和压缩数据库的改进Apriori算法[J].成都理工大学学报（自然科学版）,2015,(1):110-114,5.基金项目
国家自然科学基金资助项目（81102742）；四川省教育厅项目（12SB025）；成都中医药大学科技发展基金项目（ZRYB201147）。 （）
