BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
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Parallel Computing
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WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
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ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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Theoretical Computer Science
Mining frequent tree-like patterns in large datasets
Data & Knowledge Engineering
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Discovering frequent geometric subgraphs
Information Systems
Fast discovery of sequential patterns in large databases using effective time-indexing
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Efficient mining of sequential patterns with time constraints: Reducing the combinations
Expert Systems with Applications: An International Journal
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Mining frequent arrangements of temporal intervals
Knowledge and Information Systems
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ACM Transactions on Computer-Human Interaction (TOCHI)
gPrune: a constraint pushing framework for graph pattern mining
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining weighted sequential patterns based on length-decreasing support constraints
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
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ACM Transactions on Knowledge Discovery from Data (TKDD)
The parameterized complexity of enumerating frequent itemsets
IWPEC'06 Proceedings of the Second international conference on Parameterized and Exact Computation
Proceedings of the CUBE International Information Technology Conference
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Over the years, a variety of algorithms for finding frequentsequential patterns in very large sequential databaseshave been developed. The key feature in most of these algorithmsis that they use a constant support constraint tocontrol the inherently exponential complexity of the problem.In general, patterns that contain only a few items willtend to be interesting if they have a high support, whereaslong patterns can still be interesting even if their supportis relatively small. Ideally, we desire to have an algorithmthat finds all the frequent patterns whose support decreasesas a function of their length. In this paper we present an algorithmcalled SLPMiner, that finds all sequential patternsthat satisfy a length-decreasing support constraint. Our experimentalevaluation shows that SLPMiner achieves up totwo orders of magnitude of speedup by effectively exploitingthe length-decreasing support constraint, and that itsruntime increases gradually as the average length of the sequences(and the discovered frequent patterns) increases.