%0 Journal Article
%T A Novel Algorithm for Mining Frequent Patterns Directly in Trans-Tree<br>一种直接在Trans-树中挖掘频繁模式的新算法
%A FAN Ming WANG Bing-Zheng
%A <br>范明
%A 王秉政
%J 计算机科学
%D 2003
%I 
%X Frequent pattern mining plays an essential role in many important data mining tasks. FP-growth is a very efficient algorithm for frequent pattern mining. However, it still suffers from creating conditional FP-tree separately and recursively during the mining process. In this paper, we propose a new algorithm, called Least-Item-First Pattern Growth (LIFPG), for mining frequent patterns. LIFPG mines frequent patterns directly in Trans-tree without using any additional data structures. The key idea is that least items are always considered first when the current pattern growth. By this way, conditional sub-tree can be created directly in Trans-tree by adjusting node-links and recounting counts of some nodes. Experiments show that, in comparison with FP-Growth, our algorithm is about four times faster and saves half of memory; it also has good time and space scalability with the number of transactions, and has an excellent performance in dense dataset mining as well.
%K Data mining
%K Frequent pattern
%K Association rule<br>频繁模式
%K 关联规则
%K 数据库
%K Trans-树
%K 数据挖掘
%K 算法
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=D554458227F7AC9F&yid=D43C4A19B2EE3C0A&vid=340AC2BF8E7AB4FD&iid=5D311CA918CA9A03&sid=7555FB9CC973F695&eid=2B5DE8A23DCEED39&journal_id=1002-137X&journal_name=计算机科学&referenced_num=6&reference_num=10