GraphFind: enhancing graph searching by low support data mining techniques.

@article{citeulike:2789324, abstract = {BACKGROUND: Biomedical and chemical databases are large and rapidly growing in size. Graphs naturally model such kinds of data. To fully exploit the wealth of information in these graph databases, a key role is played by systems that search for all exact or approximate occurrences of a query graph. To deal efficiently with graph searching, advanced methods for indexing, representation and matching of graphs have been proposed. RESULTS: This paper presents GraphFind. The system implements efficient graph searching algorithms together with advanced filtering techniques that allow approximate search. It allows users to select candidate subgraphs rather than entire graphs. It implements an effective data storage based also on low-support data mining. CONCLUSIONS: GraphFind is compared with Frowns, GraphGrep and gIndex. Experiments show that GraphFind outperforms the compared systems on a very large collection of small graphs. The proposed low-support mining technique which applies to any searching system also allows a significant index space reduction.}, address = {Dipartimento di Matematica e Informatica, Universit\`{a} di Catania, Catania, 95125, Italy. ferro@dmi.unict.it}, author = {Ferro, A. and Giugno, R. and Mongiov\`{i}, M. and Pulvirenti, A. and Skripin, D. and Shasha, D. }, citeulike-article-id = {2789324}, doi = {10.1186/1471-2105-9-S4-S10}, issn = {1471-2105}, journal = {BMC bioinformatics}, keywords = {imaging}, posted-at = {2008-05-16 03:49:56}, priority = {2}, title = {GraphFind: enhancing graph searching by low support data mining techniques.}, url = {http://dx.doi.org/10.1186/1471-2105-9-S4-S10}, volume = {9 Suppl 4}, year = {2008} }

TY - JOUR ID - citeulike:2789324 L3 - citeulike-article-id:2789324 TI - GraphFind: enhancing graph searching by low support data mining techniques. JF - BMC bioinformatics VL - 9 Suppl 4 AD - Dipartimento di Matematica e Informatica, Università di Catania, Catania, 95125, Italy. ferro@dmi.unict.it SN - 1471-2105 N2 - BACKGROUND: Biomedical and chemical databases are large and rapidly growing in size. Graphs naturally model such kinds of data. To fully exploit the wealth of information in these graph databases, a key role is played by systems that search for all exact or approximate occurrences of a query graph. To deal efficiently with graph searching, advanced methods for indexing, representation and matching of graphs have been proposed. RESULTS: This paper presents GraphFind. The system implements efficient graph searching algorithms together with advanced filtering techniques that allow approximate search. It allows users to select candidate subgraphs rather than entire graphs. It implements an effective data storage based also on low-support data mining. CONCLUSIONS: GraphFind is compared with Frowns, GraphGrep and gIndex. Experiments show that GraphFind outperforms the compared systems on a very large collection of small graphs. The proposed low-support mining technique which applies to any searching system also allows a significant index space reduction. KW - imaging AU - Ferro, A AU - Giugno, R AU - Mongiovì, M AU - Pulvirenti, A AU - Skripin, D AU - Shasha, D PY - 2008/// UR - http://dx.doi.org/10.1186/1471-2105-9-S4-S10 ER -