文章基本信息

标题：Efficient Mining Algorithms of Finding Frequent Datasets
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作者：Zhou, Lijuan ; Zhang, Zhang
期刊名称：Journal of Software
印刷版ISSN：1796-217X
出版年度：2012
卷号：7
期号：4
页码：727-732
DOI：10.4304/jsw.7.4.727-732
语种：English
出版社：Academy Publisher
摘要：This work proposes an efficient mining algorithm to find maximal frequent item sets from relational database. It adapts to large datasets.Itemset is stored in list with special structure. The two main lists called itemset list and Frequent itemset list are created by scanning database once for dividing maximal itemsets into two categories depending on whether the itemsets to achieve minimum support number. Sub itemsets whose superset is in itemset list are generated by recursion to make sure that each sub itemsets appeared before its superset. As current sub itemsets being joined to frequent itemset list, its sub itemsets are pruned from the itemset list. At last, all sub itemsets whose nearest superset is in frequent itemset list are pruned from the frequent itemset list to hold all maximal frequent itemsets.We compare our algorithms and FP-Growth by two sets of time-consuming experiments to prove the superiority of our efficient algorithm both not only with increasing datasets but also with changing mini-support.
关键词：data mining;relational database;maximal frequent item sets