LEADER 05768nam 22008055 450 001 996466161503316 005 20200703152518.0 010 $a9783540318941 024 7 $a10.1007/b137601 035 $a(CKB)1000000000213082 035 $a(SSID)ssj0000318682 035 $a(PQKBManifestationID)11223466 035 $a(PQKBTitleCode)TC0000318682 035 $a(PQKBWorkID)10310556 035 $a(PQKB)10784307 035 $a(DE-He213)978-3-540-31894-1 035 $a(MiAaPQ)EBC3067540 035 $a(PPN)123095719 035 $a(EXLCZ)991000000000213082 100 $a20100925d2005 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aLocal Pattern Detection$b[electronic resource] $eInternational Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers /$fedited by Katharina Morik, Jean-Francois Boulicaut, Arno Siebes 205 $a1st ed. 2005. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2005. 215 $a1 online resource (XI, 233 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v3539 300 $a"International Seminar on Local Pattern Detection"--P. [4] of cover. 311 $a3-540-31894-1 311 $a3-540-26543-0 320 $aIncludes bibliographical references and author index. 327 $aPushing Constraints to Detect Local Patterns -- From Local to Global Patterns: Evaluation Issues in Rule Learning Algorithms -- Pattern Discovery Tools for Detecting Cheating in Student Coursework -- Local Pattern Detection and Clustering -- Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery -- Visualizing Very Large Graphs Using Clustering Neighborhoods -- Features for Learning Local Patterns in Time-Stamped Data -- Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Analysis -- Local Pattern Discovery in Array-CGH Data -- Learning with Local Models -- Knowledge-Based Sampling for Subgroup Discovery -- Temporal Evolution and Local Patterns -- Undirected Exception Rule Discovery as Local Pattern Detection -- From Local to Global Analysis of Music Time Series. 330 $aIntroduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti?c and commercial information. The need to analyze these masses of data has led to the evolution of the new ?eld knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the ?eld o?ers the opportunity to combine the expertise of di?erent ?elds intoacommonobjective.Moreover,withineach?elddiversemethodshave been developed and justi?ed with respect to di?erent quality criteria. We have toinvestigatehowthesemethods cancontributeto solvingthe problemofKDD. Traditionally, KDD was seeking to ?nd global models for the data that - plain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverageor classi?cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to ?nd only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new ?eld of local patterns. 410 0$aLecture Notes in Artificial Intelligence ;$v3539 606 $aArtificial intelligence 606 $aData structures (Computer science) 606 $aAlgorithms 606 $aMathematical statistics 606 $aDatabase management 606 $aInformation storage and retrieval 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aData Structures and Information Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/I15009 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 615 0$aArtificial intelligence. 615 0$aData structures (Computer science). 615 0$aAlgorithms. 615 0$aMathematical statistics. 615 0$aDatabase management. 615 0$aInformation storage and retrieval. 615 14$aArtificial Intelligence. 615 24$aData Structures and Information Theory. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aProbability and Statistics in Computer Science. 615 24$aDatabase Management. 615 24$aInformation Storage and Retrieval. 676 $a006.3/12 702 $aMorik$b Katharina$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBoulicaut$b Jean-Francois$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSiebes$b Arno$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aInternational Seminar on Local Pattern Detection 906 $aBOOK 912 $a996466161503316 996 $aLocal Pattern Detection$9772333 997 $aUNISA