Models of incremental concept formation
Artificial Intelligence
Spatial analogy and subsumption
ML92 Proceedings of the ninth international workshop on Machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Extracting schema from semistructured data
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
IEEE Intelligent Systems
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Representative Objects: Concise Representations of Semistructured, Hierarchial Data
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Adding Structure to Unstructured Data
ICDT '97 Proceedings of the 6th International Conference on Database Theory
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Unifying Learning Methods by Colored Digraphs
ALT '93 Proceedings of the 4th International Workshop on Algorithmic Learning Theory
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
Mining tree queries in a graph
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
Graph-Based Procedural Abstraction
Proceedings of the International Symposium on Code Generation and Optimization
Discovering frequent geometric subgraphs
Information Systems
GADDI: distance index based subgraph matching in biological networks
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Mining globally distributed frequent subgraphs in a single labeled graph
Data & Knowledge Engineering
Tracking hidden groups using communications
ISI'03 Proceedings of the 1st NSF/NIJ conference on Intelligence and security informatics
NODAR: mining globally distributed substructures from a single labeled graph
Journal of Intelligent Information Systems
A direct mining approach to efficient constrained graph pattern discovery
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
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We study the problem of finding frequent structures in semistructured data (represented as a directed labeled graph). Frequent structures are graphs that are isomorphic to a large number of subgraphs in the data graph. Frequent structures form building blocks for visual exploration and data mining of semistructured data. We overcome the inherent computational complexity of the problem by using a summary data structure to prune the search space and to provide interactive feedback. We present an experimental study of our methods operating on real datasets. The implementation of our methods is capable of operating on datasets that are two to three orders of magnitude larger than those described in prior work.