Finding what's not there: a new approach to revealing neglected conditions in software
Proceedings of the 2007 international symposium on Software testing and analysis
RAM: Randomized Approximate Graph Mining
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
FOGGER: an algorithm for graph generator discovery
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
JPMiner: mining frequent jump patterns from graph databases
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
MARGIN: Maximal frequent subgraph mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
DESSIN: mining dense subgraph patterns in a single graph
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
TGP: mining top-K frequent closed graph pattern without minimum support
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Structure and attribute index for approximate graph matching in large graphs
Information Systems
Substructure clustering: a novel mining paradigm for arbitrary data types
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
A direct mining approach to efficient constrained graph pattern discovery
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Frequent subgraph summarization with error control
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
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The exponential number of possible subgraphsmakes the problem of frequent subgraph mining a challenge. The set of maximal frequent subgraphs is much smaller to that of the set of frequent subgraphs, thus providing ample scope for pruning. MARGIN is a maximal subgraph mining algorithm that moves among promising nodes of the search space along the "border" of the infrequent and frequent subgraphs. This drastically reduces the number of candidate patterns considered in the search space. Experimental results validate the efficiency and utility of the technique proposed.