A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Algorithms for Sequential Patterns in Parallel: Hash Based Approach
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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Previous studies have presented convincing arguments that a frequent sequence mining algorithm should not mine all frequent sequences but only the closed ones because the latter leads to not only more compact yet complete result set but also better efficiency. However, frequent closed sequence mining is still challenging on stand-alone for its large size and high dimension. In this paper, an algorithm, PFCSeq, is presented for mining frequent closed sequence based on distributed-memory parallel machine, in which each processor mines local frequent closed sequence set independently using task parallelism with data parallelism approach, and only two communications are needed except that imbalance is detected. Therefore, time spent in communications is significantly reduced. In order to ensure good load balance among processors, a dynamic workload balance strategy is proposed. Experiments show that it is linearly scalable in terms of database size and the number of processors.