A parallel algorithm for computing borders
Proceedings of the 20th ACM international conference on Information and knowledge management
An efficient mining algorithm for maximal weighted frequent patterns in transactional databases
Knowledge-Based Systems
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
Efficient mining of maximal correlated weight frequent patterns
Intelligent Data Analysis
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While frequent pattern mining is fundamental for many data mining tasks, mining maximal frequent patterns efficiently is important in both theory and applications of frequent pattern mining. The fundamental challenge is how to search a large space of item combinations. Most of the existing methods search an enumeration tree of item combinations in a depth-first manner. In this paper, we develop a new technique for more efficient max-pattern mining. Our method is pattern-aware: it uses the patterns already found to schedule its future search so that many search subspaces can be pruned. We present efficient techniques to implement the new approach. As indicated by a systematic empirical study using the benchmark data sets, our new approach outperforms the currently fastest max-pattern mining algorithms FPMax* and LCM2 clearly. The source code and the executable code (on both Windows and Linux platforms) are publicly available at http://www.cs.sfu.ca/~jpei/Software/PADS.zip.