FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Frequent Quer Patterns from XML Queries
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
TreeFinder: a First Step towards XML Data Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
D(k)-index: an adaptive structural summary for graph-structured data
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Exploiting Local Similarity for Indexing Paths in Graph-Structured Data
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
TSP: Mining Top-K Closed Sequential Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
PRIX: Indexing And Querying XML Using Prüfer Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
BIDE: Efficient Mining of Frequent Closed Sequences
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Mining Closed and Maximal Frequent Subtrees from Databases of Labeled Rooted Trees
IEEE Transactions on Knowledge and Data Engineering
Exploit sequencing to accelerate hot XML query pattern mining
Proceedings of the 2006 ACM symposium on Applied computing
Efficient mining of XML query patterns for caching
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Exploit sequencing to accelerate XML twig query answering
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Mining frequent closed trees in evolving data streams
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
| Hi-index | 0.00 |
Previous research works have presented convincing arguments that a frequent pattern mining algorithm should not mine all frequent but only the closed ones because the latter leads to not only more compact yet complete result set but also better efficiency. Upon discovery of frequent closed XML query patterns, indexing and caching can be effectively adopted for query performance enhancement. Most of the previous algorithms for finding frequent patterns basically introduced a straightforward generate-and-test strategy. In this paper, we present SOLARIA*, an efficient algorithm for mining frequent closed XML query patterns without candidate maintenance and costly tree-containment checking. Efficient algorithm of sequence mining is involved in discovering frequent tree-structured patterns, which aims at replacing expensive containment testing with cheap parent-child checking in sequences. SOLARIA* deeply prunes unrelated search space for frequent pattern enumeration by parent-child relationship constraint. By a thorough experimental study on various real-life data, we demonstrate the efficiency and scalability of SOLARIA* over the previous known alternative. SOLARIA* is also linearly scalable in terms of XML queries' size.