FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Parallel sequence mining on shared-memory machines
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
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
Parallel tree-projection-based sequence mining algorithms
Parallel Computing
Frequent Closed Sequence Mining without Candidate Maintenance
IEEE Transactions on Knowledge and Data Engineering
Direct mining of discriminative and essential frequent patterns via model-based search tree
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Permu-pattern: discovery of mutable permutation patterns with proximity constraint
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Cut-and-stitch: efficient parallel learning of linear dynamical systems on smps
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast discovery of sequential patterns in large databases using effective time-indexing
Information Sciences: an International Journal
Mining Sequential Patterns with Negative Conclusions
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Efficient algorithms for incremental maintenance of closed sequential patterns in large databases
Data & Knowledge Engineering
Efficient frequent sequence mining by a dynamic strategy switching algorithm
The VLDB Journal — The International Journal on Very Large Data Bases
Event Correlations in Sensor Networks
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
Data & Knowledge Engineering
Parallel exact time series motif discovery
Euro-Par'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part II
Mining closed discriminative dyadic sequential patterns
Proceedings of the 14th International Conference on Extending Database Technology
An empirical study on mining sequential patterns in a grid computing environment
Expert Systems with Applications: An International Journal
BIDE-Based parallel mining of frequent closed sequences with mapreduce
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
Sequential pattern mining -- approaches and algorithms
ACM Computing Surveys (CSUR)
Mind the gap: large-scale frequent sequence mining
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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Discovery of sequential patterns is an essential data mining task with broad applications. Among several variations of sequential patterns, closed sequential pattern is the most useful one since it retains all the information of the complete pattern set but is often much more compact than it. Unfortunately, there is no parallel closed sequential pattern mining method proposed yet. In this paper we develop an algorithm, called Par-CSP (Parallel Closed Sequential Pattern mining), to conduct parallel mining of closed sequential patterns on a distributed memory system. Par-CSP partitions the work among the processors by exploiting the divide-and-conquer property so that the overhead of interprocessor communication is minimized. Par-CSP applies dynamic scheduling to avoid processor idling. Moreover, it employs a technique, called selective sampling to address the load imbalance problem. We implement Par-CSP using MPI on a 64-node Linux cluster. Our experimental results show that Par-CSP attains good parallelization efficiencies on various input datasets.