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An efficient mining algorithm for maximal weighted frequent patterns in transactional databases
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DOI：10.1016/j.knosys.2012.02.002.png)
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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. (C) 2012 Elsevier B.V. All rights reserved.
Keyword:
Data mining
Weighted frequent pattern mining
Maximal frequent pattern mining
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