文章基本信息

标题：An Efficient TDTR Algorithm for Mining Frequent Itemsets
本地全文：下载
作者：D.Kerana Hanirex
期刊名称：International Journal of Electronics and Computer Science Engineering
电子版ISSN：2277-1956
出版年度：2013
卷号：2
期号：1
页码：251-256
出版社：Buldanshahr : IJECSE
摘要：Research on mining frequent itemsets is one of the emerging task in data mining. The purchasing of one product when another product is purchased represents an association rule. Association rules are useful for analyzing the customer behavior. It takes an important part in shopping basket data analysis, clustering. The FP-Growth algorithm is the basic algorithm for mining association rules. This paper presents an efficient algorithm for mining frequent itemsets using Two Dimensional Transactions Reduction(TDTR) approach which reduces the original database(D) transactions to the reduced data base transactions D1 based on the min_sup count. Then for each item it finds the number of transactions that the item present and hence find the largest frequent itemset using the two dimensional approach. Using the largest item set property ,it finds the subset of frequent item sets. Thus TDTR approach reduces the number of scans in the database and hence improve the efficiency & accuracy by finding the number of association rules and reduces time to find the rules.
关键词：Data mining; Association rule; FP-Growth algorithm; frequent Itemset ;transaction reduction