Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Mining in Frequent Dynamic Subgraphs
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A Fast Method to Mine Frequent Subsequences from Graph Sequence Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Discovering descriptive rules in relational dynamic graphs
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
| Hi-index | 0.00 |
The mining of a complete set of frequent subgraphs from labeled graph data has been studied extensively Recently, much attention has been given to frequent pattern mining from graph sequences In this paper, we propose a method to improve GTRACE which mines frequent patterns called FTSs (Frequent Transformation Subsequences) from graph sequences Our performance study shows that the proposed method is efficient and scalable for mining both long and large graph sequence patterns, and is some orders of magnitude faster than the conventional method.