People

William Eberle, Tennessee Technological University

Jiawei Han, University of Illinois at Urbana-Champaign

George Karypis, University of Minnesota

Hiroshi Motoda, Osaka University

Takashi Washio, Osaka University

Software

GBAD: Graph-Based Anomaly Detection, Tennessee Technological University

gSpan: Frequent Subgraph Miner, University of Illinois at Urbana-Champaign

FSG: Frequent Subgraph Miner (part of the PAFI system), University of Minnesota

METIS: Graph Partitioner, University of Minnesota

Publications

L. Dehaspe, H. Toivonen and R. King, Finding Frequent Substructures in Chemical Compounds, Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD), 1998.

M. Deshpande, M. Kuramochi and G. Karypis, Frequent Sub-structure Based Approaches for Classifying Chemical Compounds, SIAM International Conference on Data Mining, 2003.

S. Dzeroski and L. De Raedt (editors), SIGKDD Explorations Special Issue on Multi-Relational Data Mining, SIGKDD Explorations, Volume 5, Issue 1, 2003.

M. Kuramochi and G. Karypis, Finding Frequent Patterns in a Large Sparse Graph, SIAM International Conference on Data Mining, 2004.

M. Kuramochi and G. Karypis, An Efficient Algorithm for Discovering Frequent Subgraphs, IEEE Transactions on Knowledge and Data Engineering, 2002.

M. Liquiere and J. Sallantin, Structural Machine Learning with Galois Lattice and Graphs, Proceedings of the International Conference on Machine Learning (ICML), 1998.

S. Nijssen and J. Kok, Faster Association Rules for Multiple Relations, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2001.

T. Washio and H. Motoda, State of the Art of Graph-based Data Mining, SIGKDD Explorations Special Issue on Multi-Relational Data Mining, Volume 5, Issue 1, 2003.

X. Yan and J. Han, CloseGraph: Mining Closed Frequent Graph Patterns, Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD), 2003.

X. Yan and J. Han, gSpan: Graph-Based Substructure Pattern Mining, Proceedings of the IEEE International Conference on Data Mining (IEEE ICDM), 2002.