DESSIN: mining dense subgraph patterns in a single graph
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Efficient discovery of frequent subgraph patterns in uncertain graph databases
Proceedings of the 14th International Conference on Extending Database Technology
Substructure clustering: a novel mining paradigm for arbitrary data types
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Efficient mining of correlated sequential patterns based on null hypothesis
Proceedings of the 2012 international workshop on Web-scale knowledge representation, retrieval and reasoning
Frequent subgraph summarization with error control
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
| Hi-index | 0.00 |
We propose a novel representative based subgraph mining model. A series of standards and methods are proposed to select invariants. Patterns are mapped into invariant vectors in a multidimensional space. To find qualified patterns, only a subset of frequent patterns is generated as representatives, such that every frequent pattern is close to one of the representative patterns while representative patterns are distant from each other. We devise the RING algorithm, integrating the representative selection into the pattern mining process. Meanwhile, we use R-trees to assist this mining process. Last but not least, a large number of real and synthetic datasets are employed for the empirical study, which show the benefits of the representative model and the efficiency of the RING algorithm.