The Model of Most Informative Patterns and Its Application to Knowledge Extraction from Graph Databases
Identifieur interne : 003216 ( Istex/Corpus ); précédent : 003215; suivant : 003217The Model of Most Informative Patterns and Its Application to Knowledge Extraction from Graph Databases
Auteurs : Frédéric Pennerath ; Amedeo NapoliSource :
- Lecture Notes in Computer Science [ 0302-9743 ]
Abstract
Abstract: This article introduces the class of Most Informative Patterns (MIPs) for characterizing a given dataset. MIPs form a reduced subset of non redundant closed patterns that are extracted from data thanks to a scoring function depending on domain knowledge. Accordingly, MIPs are designed for providing experts good insights on the content of datasets during data analysis. The article presents the model of MIPs and their formal properties wrt other kinds of patterns. Then, two algorithms for extracting MIPs are detailed: the first directly searches for MIPs in a dataset while the second screens MIPs from frequent patterns. The efficiencies of both algorithms are compared when applied to reference datasets. Finally the application of MIPs to labelled graphs, here molecular graphs, is discussed.
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DOI: 10.1007/978-3-642-04174-7_14
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<record><TEI wicri:istexFullTextTei="biblStruct"><teiHeader><fileDesc><titleStmt><title xml:lang="en">The Model of Most Informative Patterns and Its Application to Knowledge Extraction from Graph Databases</title><author><name sortKey="Pennerath, Frederic" sort="Pennerath, Frederic" uniqKey="Pennerath F" first="Frédéric" last="Pennerath">Frédéric Pennerath</name><affiliation><mods:affiliation>Supélec, Campus de Metz, 2 rue Édouard Belin, 57070, Metz, France</mods:affiliation></affiliation><affiliation><mods:affiliation>Orpailleur team, LORIA, BP 239, 54506, Vandoeuvre-lès-Nancy Cedex, France</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: frederic.pennerath@supelec.fr</mods:affiliation></affiliation></author><author><name sortKey="Napoli, Amedeo" sort="Napoli, Amedeo" uniqKey="Napoli A" first="Amedeo" last="Napoli">Amedeo Napoli</name><affiliation><mods:affiliation>Orpailleur team, LORIA, BP 239, 54506, Vandoeuvre-lès-Nancy Cedex, France</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: amedeo.napoli@loria.fr</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:D2B3E5D43F3FF9B024486EF6D4837D1FC4105A9B</idno><date when="2009" year="2009">2009</date><idno type="doi">10.1007/978-3-642-04174-7_14</idno><idno type="url">https://api.istex.fr/ark:/67375/HCB-XPPF2QNJ-X/fulltext.pdf</idno><idno type="wicri:Area/Istex/Corpus">003216</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">003216</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a" type="main" xml:lang="en">The Model of Most Informative Patterns and Its Application to Knowledge Extraction from Graph Databases</title><author><name sortKey="Pennerath, Frederic" sort="Pennerath, Frederic" uniqKey="Pennerath F" first="Frédéric" last="Pennerath">Frédéric Pennerath</name><affiliation><mods:affiliation>Supélec, Campus de Metz, 2 rue Édouard Belin, 57070, Metz, France</mods:affiliation></affiliation><affiliation><mods:affiliation>Orpailleur team, LORIA, BP 239, 54506, Vandoeuvre-lès-Nancy Cedex, France</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: frederic.pennerath@supelec.fr</mods:affiliation></affiliation></author><author><name sortKey="Napoli, Amedeo" sort="Napoli, Amedeo" uniqKey="Napoli A" first="Amedeo" last="Napoli">Amedeo Napoli</name><affiliation><mods:affiliation>Orpailleur team, LORIA, BP 239, 54506, Vandoeuvre-lès-Nancy Cedex, France</mods:affiliation></affiliation><affiliation><mods:affiliation>E-mail: amedeo.napoli@loria.fr</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="s" type="main" xml:lang="en">Lecture Notes in Computer Science</title><idno type="ISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="ISSN">0302-9743</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0302-9743</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Abstract: This article introduces the class of Most Informative Patterns (MIPs) for characterizing a given dataset. MIPs form a reduced subset of non redundant closed patterns that are extracted from data thanks to a scoring function depending on domain knowledge. Accordingly, MIPs are designed for providing experts good insights on the content of datasets during data analysis. The article presents the model of MIPs and their formal properties wrt other kinds of patterns. Then, two algorithms for extracting MIPs are detailed: the first directly searches for MIPs in a dataset while the second screens MIPs from frequent patterns. The efficiencies of both algorithms are compared when applied to reference datasets. 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MIPs form a reduced subset of non redundant closed patterns that are extracted from data thanks to a scoring function depending on domain knowledge. Accordingly, MIPs are designed for providing experts good insights on the content of datasets during data analysis. The article presents the model of MIPs and their formal properties wrt other kinds of patterns. Then, two algorithms for extracting MIPs are detailed: the first directly searches for MIPs in a dataset while the second screens MIPs from frequent patterns. The efficiencies of both algorithms are compared when applied to reference datasets. 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Technologies</orgName><orgName type="institution">Jožef Stefan Institute</orgName><address><street>Jamova 39</street><postCode>1000</postCode><settlement>Ljubljana</settlement><country key="SI">SLOVENIA</country></address></affiliation></editor><editor><persName><forename type="first">Dunja</forename><surname>Mladenić</surname></persName><email>dunja.mladenic@ijs.si</email><affiliation><orgName type="department">Dept. of Knowledge Technologies</orgName><orgName type="institution">Jožef Stefan Institute</orgName><address><street>Jamova 39</street><postCode>1000</postCode><settlement>Ljubljana</settlement><country key="SI">SLOVENIA</country></address></affiliation></editor><editor><persName><forename type="first">John</forename><surname>Shawe-Taylor</surname></persName><email>jst@cs.ucl.ac.uk</email><affiliation><orgName type="department">The Centre for Computational Statistics and Machine Learning Department of Computer Science</orgName><orgName type="institution">University College 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University</orgName><address><settlement>Pittsburgh</settlement><region>PA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Josef</forename><surname>Kittler</surname></persName><affiliation><orgName type="institution">University of Surrey</orgName><address><settlement>Guildford</settlement><country key="GB">UNITED KINGDOM</country></address></affiliation></editor><editor><persName><forename type="first">Jon</forename><forename type="first">M.</forename><surname>Kleinberg</surname></persName><affiliation><orgName type="institution">Cornell University</orgName><address><settlement>Ithaca</settlement><region>NY</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Friedemann</forename><surname>Mattern</surname></persName><affiliation><orgName type="institution">ETH Zurich</orgName><address><settlement>Zurich</settlement><country key="CH">SWITZERLAND</country></address></affiliation></editor><editor><persName><forename type="first">John</forename><forename type="first">C.</forename><surname>Mitchell</surname></persName><affiliation><orgName type="institution">Stanford University</orgName><address><settlement>Stanford</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Moni</forename><surname>Naor</surname></persName><affiliation><orgName type="institution">Weizmann Institute of Science</orgName><address><settlement>Rehovot</settlement><country key="IL">ISRAEL</country></address></affiliation></editor><editor><persName><forename type="first">Oscar</forename><surname>Nierstrasz</surname></persName><affiliation><orgName type="institution">University of Bern</orgName><address><settlement>Bern</settlement><country key="CH">SWITZERLAND</country></address></affiliation></editor><editor><persName><forename type="first">C.</forename><surname>Pandu Rangan</surname></persName><affiliation><orgName type="institution">Indian Institute of Technology</orgName><address><settlement>Madras</settlement><country key="IN">INDIA</country></address></affiliation></editor><editor><persName><forename type="first">Bernhard</forename><surname>Steffen</surname></persName><affiliation><orgName type="institution">University of Dortmund</orgName><address><settlement>Dortmund</settlement><country key="DE">GERMANY</country></address></affiliation></editor><editor><persName><forename type="first">Madhu</forename><surname>Sudan</surname></persName><affiliation><orgName type="institution">Massachusetts Institute of Technology</orgName><address><region>MA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Demetri</forename><surname>Terzopoulos</surname></persName><affiliation><orgName type="institution">University of California</orgName><address><settlement>Los Angeles</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Doug</forename><surname>Tygar</surname></persName><affiliation><orgName type="institution">University of California</orgName><address><settlement>Berkeley</settlement><region>CA</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Moshe</forename><forename type="first">Y.</forename><surname>Vardi</surname></persName><affiliation><orgName type="institution">Rice University</orgName><address><settlement>Houston</settlement><region>TX</region><country key="US">UNITED STATES</country></address></affiliation></editor><editor><persName><forename type="first">Gerhard</forename><surname>Weikum</surname></persName><affiliation><orgName type="institution">Max-Planck Institute of Computer Science</orgName><address><settlement>Saarbrücken</settlement><country key="DE">GERMANY</country></address></affiliation></editor><idno type="pISSN">0302-9743</idno><idno type="eISSN">1611-3349</idno><idno type="seriesID">558</idno></series></biblStruct></sourceDesc></fileDesc><profileDesc><abstract xml:lang="en"><head>Abstract</head><p>This article introduces the class of Most Informative Patterns (MIPs) for characterizing a given dataset. MIPs form a reduced subset of non redundant closed patterns that are extracted from data thanks to a scoring function depending on domain knowledge. Accordingly, MIPs are designed for providing experts good insights on the content of datasets during data analysis. The article presents the model of MIPs and their formal properties wrt other kinds of patterns. Then, two algorithms for extracting MIPs are detailed: the first directly searches for MIPs in a dataset while the second screens MIPs from frequent patterns. The efficiencies of both algorithms are compared when applied to reference datasets. Finally the application of MIPs to labelled graphs, here molecular graphs, is discussed.</p></abstract><textClass ana="subject"><keywords scheme="book-subject-collection"><list><label>SUCO11645</label><item><term>Computer Science</term></item></list></keywords></textClass><textClass ana="subject"><keywords scheme="book-subject"><list><label>I</label><item><term type="Primary">Computer Science</term></item><label>I21017</label><item><term type="Secondary" subtype="priority-1">Artificial Intelligence (incl. 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Zurich</OrgName><OrgAddress><City>Zurich</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff6"><OrgName>Stanford University</OrgName><OrgAddress><City>Stanford</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff7"><OrgName>Weizmann Institute of Science</OrgName><OrgAddress><City>Rehovot</City><Country>Israel</Country></OrgAddress></Affiliation><Affiliation ID="Aff8"><OrgName>University of Bern</OrgName><OrgAddress><City>Bern</City><Country>Switzerland</Country></OrgAddress></Affiliation><Affiliation ID="Aff9"><OrgName>Indian Institute of Technology</OrgName><OrgAddress><City>Madras</City><Country>India</Country></OrgAddress></Affiliation><Affiliation ID="Aff10"><OrgName>University of Dortmund</OrgName><OrgAddress><City>Dortmund</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff11"><OrgName>Massachusetts Institute of Technology</OrgName><OrgAddress><State>MA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff12"><OrgName>University of California</OrgName><OrgAddress><City>Los Angeles</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff13"><OrgName>University of California</OrgName><OrgAddress><City>Berkeley</City><State>CA</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff14"><OrgName>Rice University</OrgName><OrgAddress><City>Houston</City><State>TX</State><Country>USA</Country></OrgAddress></Affiliation><Affiliation ID="Aff15"><OrgName>Max-Planck Institute of Computer Science</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SeriesHeader><SubSeries><SubSeriesInfo><SubSeriesID>1244</SubSeriesID><SubSeriesPrintISSN>0302-9743</SubSeriesPrintISSN><SubSeriesElectronicISSN>1611-3349</SubSeriesElectronicISSN><SubSeriesTitle Language="En">Lecture Notes in Artificial Intelligence</SubSeriesTitle></SubSeriesInfo><SubSeriesHeader><EditorGroup><Editor AffiliationIDS="Aff16"><EditorName DisplayOrder="Western"><GivenName>Randy</GivenName><FamilyName>Goebel</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff17"><EditorName DisplayOrder="Western"><GivenName>Jörg</GivenName><FamilyName>Siekmann</FamilyName></EditorName></Editor><Editor AffiliationIDS="Aff18"><EditorName DisplayOrder="Western"><GivenName>Wolfgang</GivenName><FamilyName>Wahlster</FamilyName></EditorName></Editor><Affiliation ID="Aff16"><OrgName>University of Alberta</OrgName><OrgAddress><City>Edmonton</City><Country>Canada</Country></OrgAddress></Affiliation><Affiliation ID="Aff17"><OrgName>University of Saarland</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation><Affiliation ID="Aff18"><OrgName>DFKI and University of Saarland</OrgName><OrgAddress><City>Saarbrücken</City><Country>Germany</Country></OrgAddress></Affiliation></EditorGroup></SubSeriesHeader></SubSeries><Book Language="En"><BookInfo BookProductType="Proceedings" ContainsESM="No" Language="En" MediaType="eBook" NumberingStyle="Unnumbered" OutputMedium="All" TocLevels="0"><BookID>978-3-642-04174-7</BookID><BookTitle>Machine Learning and Knowledge Discovery in Databases</BookTitle><BookSubTitle>European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part II</BookSubTitle><BookVolumeNumber>5782</BookVolumeNumber><BookSequenceNumber>5782</BookSequenceNumber><BookDOI>10.1007/978-3-642-04174-7</BookDOI><BookTitleID>191454</BookTitleID><BookPrintISBN>978-3-642-04173-0</BookPrintISBN><BookElectronicISBN>978-3-642-04174-7</BookElectronicISBN><BookChapterCount>57</BookChapterCount><BookCopyright><CopyrightHolderName>Springer Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2009</CopyrightYear></BookCopyright><BookSubjectGroup><BookSubject Code="I" Type="Primary">Computer Science</BookSubject><BookSubject Code="I21017" Priority="1" Type="Secondary">Artificial Intelligence (incl. Robotics)</BookSubject><BookSubject Code="I18030" Priority="2" Type="Secondary">Data Mining and Knowledge Discovery</BookSubject><BookSubject Code="I18024" Priority="3" Type="Secondary">Database Management</BookSubject><BookSubject Code="I18032" Priority="4" Type="Secondary">Information Storage and Retrieval</BookSubject><BookSubject Code="I18008" Priority="5" Type="Secondary">Information Systems and Communication Service</BookSubject><BookSubject Code="I2203X" Priority="6" Type="Secondary">Pattern Recognition</BookSubject><SubjectCollection Code="SUCO11645">Computer Science</SubjectCollection></BookSubjectGroup><BookContext><SeriesID>558</SeriesID><SubSeriesID>1244</SubSeriesID></BookContext></BookInfo><BookHeader><EditorGroup><Editor AffiliationIDS="Aff19"><EditorName DisplayOrder="Western"><GivenName>Wray</GivenName><FamilyName>Buntine</FamilyName></EditorName><Contact><Email>wray.buntine@nicta.com.au</Email></Contact></Editor><Editor AffiliationIDS="Aff20"><EditorName DisplayOrder="Western"><GivenName>Marko</GivenName><FamilyName>Grobelnik</FamilyName></EditorName><Contact><Email>marko.grobelnik@ijs.si</Email></Contact></Editor><Editor AffiliationIDS="Aff20"><EditorName DisplayOrder="Western"><GivenName>Dunja</GivenName><FamilyName>Mladenić</FamilyName></EditorName><Contact><Email>dunja.mladenic@ijs.si</Email></Contact></Editor><Editor AffiliationIDS="Aff21"><EditorName DisplayOrder="Western"><GivenName>John</GivenName><FamilyName>Shawe-Taylor</FamilyName></EditorName><Contact><Email>jst@cs.ucl.ac.uk</Email></Contact></Editor><Affiliation ID="Aff19"><OrgName>NICTA, Locked Bag 8001, Canberra, 2601, Australia and Helsinki Institute of IT</OrgName><OrgAddress><Country>Finland</Country></OrgAddress></Affiliation><Affiliation ID="Aff20"><OrgDivision>Dept. of Knowledge Technologies</OrgDivision><OrgName>Jožef Stefan Institute</OrgName><OrgAddress><Street>Jamova 39</Street><Postcode>1000</Postcode><City>Ljubljana</City><Country>Slovenia</Country></OrgAddress></Affiliation><Affiliation ID="Aff21"><OrgDivision>The Centre for Computational Statistics and Machine Learning Department of Computer Science</OrgDivision><OrgName>University College London</OrgName><OrgAddress><Street>Gower St.,</Street><Postcode>WC1E 6BT</Postcode><City>London</City><Country>UK</Country></OrgAddress></Affiliation></EditorGroup></BookHeader><Part ID="Part1"><PartInfo TocLevels="0"><PartID>1</PartID><PartSequenceNumber>1</PartSequenceNumber><PartTitle>Regular Papers</PartTitle><PartChapterCount>45</PartChapterCount><PartContext><SeriesID>558</SeriesID><BookTitle>Machine Learning and Knowledge Discovery in Databases</BookTitle></PartContext></PartInfo><Chapter ID="Chap14" Language="En"><ChapterInfo ChapterType="OriginalPaper" ContainsESM="No" NumberingStyle="Unnumbered" TocLevels="0"><ChapterID>14</ChapterID><ChapterDOI>10.1007/978-3-642-04174-7_14</ChapterDOI><ChapterSequenceNumber>14</ChapterSequenceNumber><ChapterTitle Language="En">The Model of Most Informative Patterns and Its Application to Knowledge Extraction from Graph Databases</ChapterTitle><ChapterFirstPage>205</ChapterFirstPage><ChapterLastPage>220</ChapterLastPage><ChapterCopyright><CopyrightHolderName>Springer-Verlag Berlin Heidelberg</CopyrightHolderName><CopyrightYear>2009</CopyrightYear></ChapterCopyright><ChapterGrants Type="Regular"><MetadataGrant Grant="OpenAccess"></MetadataGrant><AbstractGrant Grant="OpenAccess"></AbstractGrant><BodyPDFGrant Grant="Restricted"></BodyPDFGrant><BodyHTMLGrant Grant="Restricted"></BodyHTMLGrant><BibliographyGrant Grant="Restricted"></BibliographyGrant><ESMGrant Grant="Restricted"></ESMGrant></ChapterGrants><ChapterContext><SeriesID>558</SeriesID><PartID>1</PartID><BookID>978-3-642-04174-7</BookID><BookTitle>Machine Learning and Knowledge Discovery in Databases</BookTitle></ChapterContext></ChapterInfo><ChapterHeader><AuthorGroup><Author AffiliationIDS="Aff22 Aff23"><AuthorName DisplayOrder="Western"><GivenName>Frédéric</GivenName><FamilyName>Pennerath</FamilyName></AuthorName><Contact><Email>frederic.pennerath@supelec.fr</Email></Contact></Author><Author AffiliationIDS="Aff23"><AuthorName DisplayOrder="Western"><GivenName>Amedeo</GivenName><FamilyName>Napoli</FamilyName></AuthorName><Contact><Email>amedeo.napoli@loria.fr</Email></Contact></Author><Affiliation ID="Aff22"><OrgName>Supélec</OrgName><OrgAddress><Street>Campus de Metz, 2 rue Édouard Belin</Street><Postcode>57070</Postcode><City>Metz</City><Country>France</Country></OrgAddress></Affiliation><Affiliation ID="Aff23"><OrgName>Orpailleur team, LORIA, BP 239</OrgName><OrgAddress><Postcode>54506</Postcode><City>Vandoeuvre-lès-Nancy Cedex</City><Country>France</Country></OrgAddress></Affiliation></AuthorGroup><Abstract ID="Abs1" Language="En"><Heading>Abstract</Heading><Para>This article introduces the class of Most Informative Patterns (MIPs) for characterizing a given dataset. MIPs form a reduced subset of non redundant closed patterns that are extracted from data thanks to a scoring function depending on domain knowledge. Accordingly, MIPs are designed for providing experts good insights on the content of datasets during data analysis. The article presents the model of MIPs and their formal properties wrt other kinds of patterns. Then, two algorithms for extracting MIPs are detailed: the first directly searches for MIPs in a dataset while the second screens MIPs from frequent patterns. The efficiencies of both algorithms are compared when applied to reference datasets. Finally the application of MIPs to labelled graphs, here molecular graphs, is discussed.</Para></Abstract></ChapterHeader><NoBody></NoBody></Chapter></Part></Book></Series></Publisher></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>The Model of Most Informative Patterns and Its Application to Knowledge Extraction from Graph Databases</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>The Model of Most Informative Patterns and Its Application to Knowledge Extraction from Graph Databases</title></titleInfo><name type="personal"><namePart type="given">Frédéric</namePart><namePart type="family">Pennerath</namePart><affiliation>Supélec, Campus de Metz, 2 rue Édouard Belin, 57070, Metz, France</affiliation><affiliation>Orpailleur team, LORIA, BP 239, 54506, Vandoeuvre-lès-Nancy Cedex, France</affiliation><affiliation>E-mail: frederic.pennerath@supelec.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Amedeo</namePart><namePart type="family">Napoli</namePart><affiliation>Orpailleur team, LORIA, BP 239, 54506, Vandoeuvre-lès-Nancy Cedex, France</affiliation><affiliation>E-mail: amedeo.napoli@loria.fr</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre displayLabel="OriginalPaper" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" type="conference" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-BFHXPBJJ-3">conference</genre><originInfo><publisher>Springer Berlin Heidelberg</publisher><place><placeTerm type="text">Berlin, Heidelberg</placeTerm></place><dateIssued encoding="w3cdtf">2009</dateIssued><copyrightDate encoding="w3cdtf">2009</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract lang="en">Abstract: This article introduces the class of Most Informative Patterns (MIPs) for characterizing a given dataset. MIPs form a reduced subset of non redundant closed patterns that are extracted from data thanks to a scoring function depending on domain knowledge. Accordingly, MIPs are designed for providing experts good insights on the content of datasets during data analysis. The article presents the model of MIPs and their formal properties wrt other kinds of patterns. Then, two algorithms for extracting MIPs are detailed: the first directly searches for MIPs in a dataset while the second screens MIPs from frequent patterns. The efficiencies of both algorithms are compared when applied to reference datasets. Finally the application of MIPs to labelled graphs, here molecular graphs, is discussed.</abstract><relatedItem type="host"><titleInfo><title>Machine Learning and Knowledge Discovery in Databases</title><subTitle>European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part II</subTitle></titleInfo><name type="personal"><namePart type="given">Wray</namePart><namePart type="family">Buntine</namePart><affiliation>NICTA, Locked Bag 8001, Canberra, 2601, Australia and Helsinki Institute of IT, Finland</affiliation><affiliation>E-mail: wray.buntine@nicta.com.au</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Marko</namePart><namePart type="family">Grobelnik</namePart><affiliation>Dept. of Knowledge Technologies, Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia</affiliation><affiliation>E-mail: marko.grobelnik@ijs.si</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Dunja</namePart><namePart type="family">Mladenić</namePart><affiliation>Dept. of Knowledge Technologies, Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia</affiliation><affiliation>E-mail: dunja.mladenic@ijs.si</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="family">Shawe-Taylor</namePart><affiliation>The Centre for Computational Statistics and Machine Learning Department of Computer Science, University College London, Gower St.,, WC1E 6BT, London, UK</affiliation><affiliation>E-mail: jst@cs.ucl.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="book-series" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0G6R5W5T-Z">book-series</genre><originInfo><publisher>Springer</publisher><copyrightDate encoding="w3cdtf">2009</copyrightDate><issuance>monographic</issuance></originInfo><subject><genre>Book-Subject-Collection</genre><topic authority="SpringerSubjectCodes" authorityURI="SUCO11645">Computer Science</topic></subject><subject><genre>Book-Subject-Group</genre><topic authority="SpringerSubjectCodes" authorityURI="I">Computer Science</topic><topic authority="SpringerSubjectCodes" authorityURI="I21017">Artificial Intelligence (incl. Robotics)</topic><topic authority="SpringerSubjectCodes" authorityURI="I18030">Data Mining and Knowledge Discovery</topic><topic authority="SpringerSubjectCodes" authorityURI="I18024">Database Management</topic><topic authority="SpringerSubjectCodes" authorityURI="I18032">Information Storage and Retrieval</topic><topic authority="SpringerSubjectCodes" authorityURI="I18008">Information Systems and Communication Service</topic><topic authority="SpringerSubjectCodes" authorityURI="I2203X">Pattern Recognition</topic></subject><identifier type="DOI">10.1007/978-3-642-04174-7</identifier><identifier type="ISBN">978-3-642-04173-0</identifier><identifier type="eISBN">978-3-642-04174-7</identifier><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="BookTitleID">191454</identifier><identifier type="BookID">978-3-642-04174-7</identifier><identifier type="BookChapterCount">57</identifier><identifier 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type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Josef</namePart><namePart type="family">Kittler</namePart><affiliation>University of Surrey, Guildford, UK</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Jon</namePart><namePart type="given">M.</namePart><namePart type="family">Kleinberg</namePart><affiliation>Cornell University, Ithaca, NY, USA</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Friedemann</namePart><namePart type="family">Mattern</namePart><affiliation>ETH Zurich, Zurich, Switzerland</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="given">C.</namePart><namePart type="family">Mitchell</namePart><affiliation>Stanford University, Stanford, CA, USA</affiliation><role><roleTerm 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Finland</affiliation><affiliation>E-mail: wray.buntine@nicta.com.au</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Marko</namePart><namePart type="family">Grobelnik</namePart><affiliation>Dept. of Knowledge Technologies, Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia</affiliation><affiliation>E-mail: marko.grobelnik@ijs.si</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">Dunja</namePart><namePart type="family">Mladenić</namePart><affiliation>Dept. of Knowledge Technologies, Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia</affiliation><affiliation>E-mail: dunja.mladenic@ijs.si</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="family">Shawe-Taylor</namePart><affiliation>The Centre for Computational Statistics and Machine Learning Department of Computer Science, University College London, Gower St.,, WC1E 6BT, London, UK</affiliation><affiliation>E-mail: jst@cs.ucl.ac.uk</affiliation><role><roleTerm type="text">editor</roleTerm></role></name><genre type="sub-series"></genre><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SubSeriesID">1244</identifier></relatedItem><identifier type="ISSN">0302-9743</identifier><identifier type="eISSN">1611-3349</identifier><identifier type="SeriesID">558</identifier><recordInfo><recordOrigin>Springer Berlin Heidelberg, 2009</recordOrigin></recordInfo></relatedItem><identifier type="istex">D2B3E5D43F3FF9B024486EF6D4837D1FC4105A9B</identifier><identifier type="ark">ark:/67375/HCB-XPPF2QNJ-X</identifier><identifier type="DOI">10.1007/978-3-642-04174-7_14</identifier><identifier type="ChapterID">14</identifier><identifier type="ChapterID">Chap14</identifier><accessCondition type="use and reproduction" 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