计算机技术与发展2018，Vol.28Issue(4)：71-76,81,7.DOI:10.3969/j.issn.1673-629X.2018.04.015
一种基于SOM划分的FP-growth算法
A FP-growth Algorithm Based on SOM Partition
摘要
Abstract
FP-growth algorithm can only handle smaller data sets,and can't do much in the face of massive data sets.For this,we im-prove the mining process of FP-growth and propose a FP-growth algorithm based on SOM partition.In the data preprocessing,each transaction in the original data is normalized to the same dimension.Considering the difficulty of large data sets processing,systematic sampling methods are used to extract representative samples from large data sets firstly.Because transactions with frequent items have smaller Euclidean distances,these samples are used to do SOM cluster analysis.The large data sets are divided into several subsets accord-ing to the clustering results.In each subset FP-growth algorithm is executed in parallel,and association rules are mined.The mining result of the subset is combined to get the total association rules. The experiments show that the improved algorithm reduces the memory con-sumption,shortens the time of data mining,and increases the capacity and efficiency to mass data with a good speedup.关键词
FP-growth/自组织映射/数据挖掘/聚类/数据划分Key words
FP-growth/SOM/data mining/cluster/data partitioning分类
信息技术与安全科学引用本文复制引用
郏奎奎,刘海滨..一种基于SOM划分的FP-growth算法[J].计算机技术与发展,2018,28(4):71-76,81,7.基金项目
国家自然科学基金重点支持项目(U150120175) （U150120175）
