An efficient mining algorithm for maximal weighted frequent patterns in transactional databases

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• 作者：Unil Yun^a ; ^{yunei@chungbuk.ac.kr} ; Hyeonil Shin^b ; ^{hyeonil.shin@lge.com} ; Keun Ho Ryu^a ; ^{khryu@dblab.chungbuk.ac.kr} ; EunChul Yoon^c ; ^{ecyoon@konkuk.ac.kr}
• 关键词：Data mining ; Weighted frequent pattern mining ; Maximal frequent pattern mining ; Vertical bitmap ; Prefix tree
• 刊名：Knowledge-Based Systems
• 出版年：2012
• 出版时间：September, 2012
• 年：2012
• 卷：33
• 期：Complete
• 页码：53-64
• 全文大小：1407 K

文摘

In the field of data mining, there have been many studies on mining frequent patterns due to its broad applications in mining association rules, correlations, sequential patterns, constraint-based frequent patterns, graph patterns, emerging patterns, and many other data mining tasks. We present a new algorithm for mining maximal weighted frequent patterns from a transactional database. Our mining paradigm prunes unimportant patterns and reduces the size of the search space. However, maintaining the anti-monotone property without loss of information should be considered, and thus our algorithm prunes weighted infrequent patterns and uses a prefix-tree with weight-descending order. In comparison, a previous algorithm, MAFIA, exponentially scales to the longest pattern length. Our algorithm outperformed MAFIA in a thorough experimental analysis on real data. In addition, our algorithm is more efficient and scalable.