【摘 要】
：
Mining frequent itemsets from large databases has played an essential role inmany data mining tasks. It is also important to maintain the discovered frequent it
【机 构】
：
School of Electronics Engineering and Computer Science,College of Software,Graduate School of Inform
【基金项目】
：
教育部留学回国人员科研启动基金;国家重点基础研究发展计划(973计划)
论文部分内容阅读
Mining frequent itemsets from large databases has played an essential role inmany data mining tasks. It is also important to maintain the discovered frequent itemsets forthese data mining tasks when the database is updated. All algorithms proposed so far for the maintenance of discovered frequent itemsets are only performed with a fixed minimum support,which is the same as that used to obtain the discovered frequent itemsets. That is, users cannot change the minimum support even if the new results are unsatisfactory to the users. In thispaper two new complementary algorithms, FMP (First Maintaining Process) and RMP (Repeated Maintaining Process), are proposed to maintain discovered frequent itemsets in the case that new transaction data are added to a transaction database. Both algorithms allow users to change theminimum support for the maintenance processes. FMP is used for the first maintaining process, andwhen the result derived from the FMP is unsatisfactory, RMP will be performed repeatedly untilsatisfactory results are obtained. The proposed algorithms re-use the previous results to cut downthe cost of maintenance. Extensive experiments have been conducted to assess the performance of the algorithms. The experimental results show that the proposed algorithms are very resultful compared with the previous mining and maintenance algorithms for maintenance of discovered frequent itemsets.
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