Mining frequent correlated graphs with a new measure
Identifieur interne : 004058 ( Main/Exploration ); précédent : 004057; suivant : 004059Mining frequent correlated graphs with a new measure
Auteurs : Md. Samiullah [Bangladesh] ; CHOWDHURY FARHAN AHMED [France] ; Anna Fariha [Bangladesh] ; Md. Rafiqul Islam [Australie] ; Nicolas Lachiche [France]Source :
- Expert systems with applications [ 0957-4174 ] ; 2014.
Descripteurs français
- Pascal (Inist)
- Fouille donnée, Analyse donnée, Sûreté fonctionnement, Système réparti, Réseau ordinateur, Base de données répartie, Chimie informatique, Bioinformatique, Traitement image, Intelligence artificielle, Apprentissage(intelligence artificielle), Extraction connaissances, Indice aptitude, Réseau social, Répartition spatiale, Cartographie, Base donnée chimique, Théorie graphe, Modélisation, Base de données, Temps exécution, Gestion mémoire, ., Base donnée très grande, Base de données orientée graphe.
- Wicri :
- topic : Intelligence artificielle, Cartographie, Base de données.
English descriptors
- KwdEn :
- Artificial intelligence, Bioinformatics, Capability index, Cartography, Chemical databasis, Computational chemistry, Computer network, Data analysis, Data mining, Database, Dependability, Distributed database, Distributed system, Execution time, Graph database, Graph theory, Image processing, Knowledge extraction, Learning (artificial intelligence), Modeling, Social network, Spatial distribution, Storage management, Very large databases.
Abstract
Correlation mining is recognized as one of the most important data mining tasks for its capability to identify underlying dependencies between objects. On the other hand, graph-based data mining techniques are increasingly applied to handle large datasets due to their capability of modeling various non-traditional domains representing real-life complex scenarios such as social/computer networks, map/spatial databases, chemical-informatics domain, bio-informatics, image processing and machine learning. To extract useful knowledge from large amount of spurious patterns, correlation measures are used. Nonetheless, existing graph based correlation mining approaches are unable to capture effective correlations in graph databases. Hence, we have concentrated on graph correlation mining and proposed a new graph correlation measure, gConfidence, to discover more useful graph patterns. Moreover, we have developed an efficient algorithm, CGM (Correlated Graph Mining), to find the correlated graphs in graph databases. The performance of our scheme was extensively analyzed in several real-life and synthetic databases based on runtime and memory consumption, then compared with existing graph correlation mining algorithms, which proved that CGM is scalable with respect to required processing time and memory consumption and outperforms existing approaches by a factor of two in speed of mining correlations.
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream PascalFrancis, to step Corpus: 000479
- to stream PascalFrancis, to step Curation: 005982
- to stream PascalFrancis, to step Checkpoint: 000195
- to stream Main, to step Merge: 004119
- to stream Main, to step Curation: 004058
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en" level="a">Mining frequent correlated graphs with a new measure</title><author><name sortKey="Samiullah, Md" sort="Samiullah, Md" uniqKey="Samiullah M" first="Md." last="Samiullah">Md. Samiullah</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Computer Science and Engineering, University of Dhaka</s1><s3>BGD</s3><sZ>1 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>Bangladesh</country><wicri:noRegion>Department of Computer Science and Engineering, University of Dhaka</wicri:noRegion></affiliation></author><author><name sortKey="Chowdhury Farhan Ahmed" sort="Chowdhury Farhan Ahmed" uniqKey="Chowdhury Farhan Ahmed" last="Chowdhury Farhan Ahmed">CHOWDHURY FARHAN AHMED</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>ICube Laboratory, University of Strasbourg</s1><s3>FRA</s3><sZ>2 aut.</sZ><sZ>5 aut.</sZ></inist:fA14><country>France</country><wicri:noRegion>University of Strasbourg</wicri:noRegion><wicri:noRegion>ICube Laboratory, University of Strasbourg</wicri:noRegion></affiliation></author><author><name sortKey="Fariha, Anna" sort="Fariha, Anna" uniqKey="Fariha A" first="Anna" last="Fariha">Anna Fariha</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Computer Science and Engineering, University of Dhaka</s1><s3>BGD</s3><sZ>1 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>Bangladesh</country><wicri:noRegion>Department of Computer Science and Engineering, University of Dhaka</wicri:noRegion></affiliation></author><author><name sortKey="Rafiqul Islam, Md" sort="Rafiqul Islam, Md" uniqKey="Rafiqul Islam M" first="Md." last="Rafiqul Islam">Md. Rafiqul Islam</name><affiliation wicri:level="1"><inist:fA14 i1="03"><s1>School of Computing and Mathematics, Charles Sturt University</s1><s3>AUS</s3><sZ>4 aut.</sZ></inist:fA14><country>Australie</country><wicri:noRegion>School of Computing and Mathematics, Charles Sturt University</wicri:noRegion></affiliation></author><author><name sortKey="Lachiche, Nicolas" sort="Lachiche, Nicolas" uniqKey="Lachiche N" first="Nicolas" last="Lachiche">Nicolas Lachiche</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>ICube Laboratory, University of Strasbourg</s1><s3>FRA</s3><sZ>2 aut.</sZ><sZ>5 aut.</sZ></inist:fA14><country>France</country><wicri:noRegion>University of Strasbourg</wicri:noRegion><wicri:noRegion>ICube Laboratory, University of Strasbourg</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">14-0105312</idno><date when="2014">2014</date><idno type="stanalyst">PASCAL 14-0105312 INIST</idno><idno type="RBID">Pascal:14-0105312</idno><idno type="wicri:Area/PascalFrancis/Corpus">000479</idno><idno type="wicri:Area/PascalFrancis/Curation">005982</idno><idno type="wicri:Area/PascalFrancis/Checkpoint">000195</idno><idno type="wicri:explorRef" wicri:stream="PascalFrancis" wicri:step="Checkpoint">000195</idno><idno type="wicri:doubleKey">0957-4174:2014:Samiullah M:mining:frequent:correlated</idno><idno type="wicri:Area/Main/Merge">004119</idno><idno type="wicri:Area/Main/Curation">004058</idno><idno type="wicri:Area/Main/Exploration">004058</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Mining frequent correlated graphs with a new measure</title><author><name sortKey="Samiullah, Md" sort="Samiullah, Md" uniqKey="Samiullah M" first="Md." last="Samiullah">Md. Samiullah</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Computer Science and Engineering, University of Dhaka</s1><s3>BGD</s3><sZ>1 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>Bangladesh</country><wicri:noRegion>Department of Computer Science and Engineering, University of Dhaka</wicri:noRegion></affiliation></author><author><name sortKey="Chowdhury Farhan Ahmed" sort="Chowdhury Farhan Ahmed" uniqKey="Chowdhury Farhan Ahmed" last="Chowdhury Farhan Ahmed">CHOWDHURY FARHAN AHMED</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>ICube Laboratory, University of Strasbourg</s1><s3>FRA</s3><sZ>2 aut.</sZ><sZ>5 aut.</sZ></inist:fA14><country>France</country><wicri:noRegion>University of Strasbourg</wicri:noRegion><wicri:noRegion>ICube Laboratory, University of Strasbourg</wicri:noRegion></affiliation></author><author><name sortKey="Fariha, Anna" sort="Fariha, Anna" uniqKey="Fariha A" first="Anna" last="Fariha">Anna Fariha</name><affiliation wicri:level="1"><inist:fA14 i1="01"><s1>Department of Computer Science and Engineering, University of Dhaka</s1><s3>BGD</s3><sZ>1 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>Bangladesh</country><wicri:noRegion>Department of Computer Science and Engineering, University of Dhaka</wicri:noRegion></affiliation></author><author><name sortKey="Rafiqul Islam, Md" sort="Rafiqul Islam, Md" uniqKey="Rafiqul Islam M" first="Md." last="Rafiqul Islam">Md. Rafiqul Islam</name><affiliation wicri:level="1"><inist:fA14 i1="03"><s1>School of Computing and Mathematics, Charles Sturt University</s1><s3>AUS</s3><sZ>4 aut.</sZ></inist:fA14><country>Australie</country><wicri:noRegion>School of Computing and Mathematics, Charles Sturt University</wicri:noRegion></affiliation></author><author><name sortKey="Lachiche, Nicolas" sort="Lachiche, Nicolas" uniqKey="Lachiche N" first="Nicolas" last="Lachiche">Nicolas Lachiche</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>ICube Laboratory, University of Strasbourg</s1><s3>FRA</s3><sZ>2 aut.</sZ><sZ>5 aut.</sZ></inist:fA14><country>France</country><wicri:noRegion>University of Strasbourg</wicri:noRegion><wicri:noRegion>ICube Laboratory, University of Strasbourg</wicri:noRegion></affiliation></author></analytic><series><title level="j" type="main">Expert systems with applications</title><title level="j" type="abbreviated">Expert syst. appl.</title><idno type="ISSN">0957-4174</idno><imprint><date when="2014">2014</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Expert systems with applications</title><title level="j" type="abbreviated">Expert syst. appl.</title><idno type="ISSN">0957-4174</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Artificial intelligence</term><term>Bioinformatics</term><term>Capability index</term><term>Cartography</term><term>Chemical databasis</term><term>Computational chemistry</term><term>Computer network</term><term>Data analysis</term><term>Data mining</term><term>Database</term><term>Dependability</term><term>Distributed database</term><term>Distributed system</term><term>Execution time</term><term>Graph database</term><term>Graph theory</term><term>Image processing</term><term>Knowledge extraction</term><term>Learning (artificial intelligence)</term><term>Modeling</term><term>Social network</term><term>Spatial distribution</term><term>Storage management</term><term>Very large databases</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Fouille donnée</term><term>Analyse donnée</term><term>Sûreté fonctionnement</term><term>Système réparti</term><term>Réseau ordinateur</term><term>Base de données répartie</term><term>Chimie informatique</term><term>Bioinformatique</term><term>Traitement image</term><term>Intelligence artificielle</term><term>Apprentissage(intelligence artificielle)</term><term>Extraction connaissances</term><term>Indice aptitude</term><term>Réseau social</term><term>Répartition spatiale</term><term>Cartographie</term><term>Base donnée chimique</term><term>Théorie graphe</term><term>Modélisation</term><term>Base de données</term><term>Temps exécution</term><term>Gestion mémoire</term><term>.</term><term>Base donnée très grande</term><term>Base de données orientée graphe</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Intelligence artificielle</term><term>Cartographie</term><term>Base de données</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Correlation mining is recognized as one of the most important data mining tasks for its capability to identify underlying dependencies between objects. On the other hand, graph-based data mining techniques are increasingly applied to handle large datasets due to their capability of modeling various non-traditional domains representing real-life complex scenarios such as social/computer networks, map/spatial databases, chemical-informatics domain, bio-informatics, image processing and machine learning. To extract useful knowledge from large amount of spurious patterns, correlation measures are used. Nonetheless, existing graph based correlation mining approaches are unable to capture effective correlations in graph databases. Hence, we have concentrated on graph correlation mining and proposed a new graph correlation measure, gConfidence, to discover more useful graph patterns. Moreover, we have developed an efficient algorithm, CGM (Correlated Graph Mining), to find the correlated graphs in graph databases. The performance of our scheme was extensively analyzed in several real-life and synthetic databases based on runtime and memory consumption, then compared with existing graph correlation mining algorithms, which proved that CGM is scalable with respect to required processing time and memory consumption and outperforms existing approaches by a factor of two in speed of mining correlations.</div></front></TEI><affiliations><list><country><li>Australie</li><li>Bangladesh</li><li>France</li></country></list><tree><country name="Bangladesh"><noRegion><name sortKey="Samiullah, Md" sort="Samiullah, Md" uniqKey="Samiullah M" first="Md." last="Samiullah">Md. Samiullah</name></noRegion><name sortKey="Fariha, Anna" sort="Fariha, Anna" uniqKey="Fariha A" first="Anna" last="Fariha">Anna Fariha</name></country><country name="France"><noRegion><name sortKey="Chowdhury Farhan Ahmed" sort="Chowdhury Farhan Ahmed" uniqKey="Chowdhury Farhan Ahmed" last="Chowdhury Farhan Ahmed">CHOWDHURY FARHAN AHMED</name></noRegion><name sortKey="Lachiche, Nicolas" sort="Lachiche, Nicolas" uniqKey="Lachiche N" first="Nicolas" last="Lachiche">Nicolas Lachiche</name></country><country name="Australie"><noRegion><name sortKey="Rafiqul Islam, Md" sort="Rafiqul Islam, Md" uniqKey="Rafiqul Islam M" first="Md." last="Rafiqul Islam">Md. Rafiqul Islam</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Asie/explor/AustralieFrV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 004058 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 004058 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Wicri/Asie
|area= AustralieFrV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= Pascal:14-0105312
|texte= Mining frequent correlated graphs with a new measure
}}
| This area was generated with Dilib version V0.6.33. |  |