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Graphs AcGM Publication Year: 2002 Purpose: enumerate all connected frequent subgraphs in a set of graphs, optionally restricted to induced subgraphs. History: modification of AGM WEBPAGE Akihiro Inokuchi, Takashi Washio, Kunio Nishimura, Hiroshi Motoda. A Fast Algorithm for Mining Frequent Connected Subgraphs. IBM Research, Tokyo Research Laboratory, 10 pages, 2002. ADI-Mine Publication Year: 2004 Purpose: enumerate all frequent induced subgraphs in a set of graphs. History: modification of gSpan for small graphs and large numbers of labels Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda. An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In: Principles of Knowledge Discovery and Data Mining (PKDD2000), pages 13-23, 2000. Chen Wang, Wei Wang, Jian Pei, Yongtai Zhu, Baile Shi. Scalable Mining Large Disk-Based Graph Databases. In: Proceedings of the 2004 Conference on Knowledge Discovery and Data Mining (SIGKDD2004), 2004. AGM Publication Years: 2000-2003 Purpose: enumerate all frequent induced subgraphs in a set of graphs. Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda. An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In: Principles of Knowledge Discovery and Data Mining (PKDD2000), pages 13-23, 2000. Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda. Complete Mining of Frequent Patterns from Graphs: Mining Graph Data. In: Machine Learning, pages 321-354, 2003. CloseGraph Publication Year: 2003 Purpose: enumerate all connected frequent closed subgraphs in a set of graphs. History: modification of gSpan WEBPAGE Xifeng Yan, Jiawei Han. CloseGraph: Mining Closed Frequent Graph Patterns. In: Proceedings of the 2003 Conference on Knowledge Discovery and Data Mining (SIGKDD2003), 2003. DSPM Publication Year: 2004 Purpose: enumerate all connected frequent closed subgraphs in a set of graphs. History: modification of gSpan with unclear advantages M. Cohen, E. Gudes. Diagonally Subgraphs Pattern Mining. In: Proceedings of the 9th ACM SIGMOD Workshop on Research issues in data mining and knowledge discovery, 2004. FreeTreeMiner Publication Years: 2003-2004 Purpose: enumerate all free subtrees in a set of graphs that satisfy constraints specified by the user, optionally using version spaces if other constraints than minimum frequency constraints are used. Ulrich Rückert, Stefan Kramer. Generalized Version Space Trees. In: Proceedings of the 2nd International Workshop on Knowledge Discovery in Inductive Databases (KDID2004), 2003. Ulrich Rückert, Stefan Kramer. Frequent Free Tree Discovery in Graph Data. In: Special Track on Data Mining, ACM Symposium on Applied Computing (SAC2004), 2004. Frequent Path based GraphMiner Publication Year: 2002-2004 Purpose: enumerate all frequent induced subgraphs (labeled or unlabeled) in a set of graphs, where an embedding is counted only if it is `edge disjoint' with other embeddings. N. Vanetik, E. Gudes, S.E. Shimony. Computing Frequent Graph Patterns from Semistructured Data. In: Proceedings of the International Conference on Data Mining 2002 (ICDM2002), 2002. N. Vanetik, E. Gudes. Mining Frequent Labeled and Partially Labeled Graph Patterns.In: Proceedings of the International Conference on Data Engineering 2004 (ICDE2004), 2004. FFSM Publication Year: 2003 Purpose: enumerate all connected frequent subgraphs in a set of graphs. HOMEPAGE. Luke Huan, Wei Wang, Jan Prins. Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism. In: Proceedings of the 2003 International Conference on Data Mining (ICDM2003), 2003. FSG(Pafi) Publication Years: 2001-2002 Purpose: enumerate all connected frequent subgraphs in a set of graphs. WEBPAGE Michihiro Kuramochi, George Karypis, Frequent Subgraph Discovery. In: Proceedings of the 2001 International Conference on Data Mining (ICDM2001), 2001. Michihiro Kuramochi, George Karypis, An Efficient Algorithm for Discovering Frequent Subgraphs. University of Minesota, Technical Report 02-026, 2002. Gaston Publication Year: 2004 Purpose: enumerate all connected frequent subgraphs in a set of graphs. HOMEPAGE Generalized AcGM Publication Year: 2004 Purpose: enumerate all connected frequent subgraphs in a set of graphs, where a hierarchy is available for the labels. History: modification of AcGM Akihiro Inokuchi. Mining Generalized Substructures from a Set of Labeled Graphs. In: Proceedings of the ICDM2004, 2004. Akihiro Inokuchi. Mining Generalized Substructures from a Set of Labeled Graphs. IBM Research, Tokyo Research Laboratory, 2004. gFSG Publication Year: 2002 Purpose: enumerate all geometric frequent subgraphs in a set of graphs. Michihiro Kuramochi, George Karypis. Discovering Frequent Geometric Subgraphs. In: Proceedings of the 2002 International Conference on Data Mining (ICDM2002), 2002. gSpan Publication Years: 2002-2003 Purpose: enumerate all connected frequent subgraphs in a set of graphs. WEBPAGE Xifeng Yan, Jiawei Han. gSpan: Graph-Based Substructure Pattern Mining. In: Proceedings of the 2002 International Conference on Data Mining (ICDM2002), 2002. Xifeng Yan, Jiawei Han. gSpan: Graph-Based Substructure Pattern Mining, Expanded Version. UIUC Technical Report, UIUCDCS-R-2002-2296, 2002. Xifeng Yan, Jiawei Han. CloseGraph: Mining Closed Frequent Graph Patterns. In: Proceedings of the 2003 Conference on Knowledge Discovery and Data Mining (SIGKDD2003), 2003. SiGraM(Pafi) Publication Year: 2004 Purpose: enumerate all non-overlapping connected frequent subgraphs in one large graph WEBPAGE Michihiro Kuramochi, George Karypis, Finding Frequent Patterns in a Large Sparse Graph. In: Proceedings of the 2004 SIAM Data Mining Conference, 2004. Spin Publication Year: 2004 Purpose: enumerate all maximal connected frequent subgraphs in a set of graphs. History: modification of FFSM DOWNLOAD Luke Huan, Wei Wang, Jan Prins, SPIN: Mining Maximal Frequent Subgraphs from Graph Databases. In: Proceedings of the 2004 Conference on Knowledge Discovery and Data Mining (SIGKDD2004), 2004.