A novel approach for mining maximal frequent patterns

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• 作者：Bay Vo^a ; ^b ; ^{bayvodinh@gmail.com} ; Sang Pham^c ; ^{sangppt@gmail.com} ; Tuong Le^d ; ^e ; ^{lecungtuong@tdt.edu.vn} ; ^{tuonglecung@gmail.com} ; Zhi-Hong Deng^f ; ^{zhdeng@cis.pku.edu.cn}
• 关键词：Data mining ; Pattern mining ; Maximal frequent patterns ; N-list structure ; Pruning technique
• 刊名：Expert Systems with Applications
• 出版年：2017
• 出版时间：1 May 2017
• 年：2017
• 卷：73
• 期：Complete
• 页码：178-186
• 全文大小：2795 K
• 卷排序：73

文摘

An N-list structure is used to compress the dataset for mining Maximal Frequent Patterns. A pruning technique is then proposed and used in INLA-MFP algorithm to improve the runtime and memory usage. Experiments were conducted to show that INLA-MFP outperforms well-known algorithms.