LEADER 05959nam 22008175 450 001 996465439103316 005 20200703220621.0 010 $a3-540-45728-3 024 7 $a10.1007/3-540-45728-3 035 $a(CKB)1000000000211836 035 $a(SSID)ssj0000325573 035 $a(PQKBManifestationID)11280389 035 $a(PQKBTitleCode)TC0000325573 035 $a(PQKBWorkID)10323969 035 $a(PQKB)10328272 035 $a(DE-He213)978-3-540-45728-2 035 $a(MiAaPQ)EBC3073158 035 $a(PPN)155235753 035 $a(EXLCZ)991000000000211836 100 $a20121227d2002 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aPattern Detection and Discovery$b[electronic resource] $eESF Exploratory Workshop, London, UK, September 16-19, 2002. /$fedited by David J Hand, Niall, M. Adams, Richard J. Bolton 205 $a1st ed. 2002. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2002. 215 $a1 online resource (XII, 232 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v2447 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-540-44148-4 327 $aGeneral Issues -- Pattern Detection and Discovery -- Detecting Interesting Instances -- Complex Data: Mining Using Patterns -- Determining Hit Rate in Pattern Search -- An Unsupervised Algorithm for Segmenting Categorical Timeseries into Episodes -- If You Can?t See the Pattern, Is It There? -- Association Rules -- Dataset Filtering Techniques in Constraint-Based Frequent Pattern Mining -- Concise Representations of Association Rules -- Constraint-Based Discovery and Inductive Queries: Application to Association Rule Mining -- Relational Association Rules: Getting Warmer -- Text and Web Mining -- Mining Text Data: Special Features and Patterns -- Modelling and Incorporating Background Knowledge in theWeb Mining Process -- Modeling Information in Textual Data Combining Labeled and Unlabeled Data -- Discovery of Frequent Word Sequences in Text -- Applications -- Pattern Detection and Discovery: The Case of Music Data Mining -- Discovery of Core Episodes from Sequences -- Patterns of Dependencies in Dynamic Multivariate Data. 330 $aThe collation of large electronic databases of scienti?c and commercial infor- tion has led to a dramatic growth of interest in methods for discovering struc- res in such databases. These methods often go under the general name of data mining. One important subdiscipline within data mining is concerned with the identi?cation and detection of anomalous, interesting, unusual, or valuable - cords or groups of records, which we call patterns. Familiar examples are the detection of fraud in credit-card transactions, of particular coincident purchases in supermarket transactions, of important nucleotide sequences in gene sequence analysis, and of characteristic traces in EEG records. Tools for the detection of such patterns have been developed within the data mining community, but also within other research communities, typically without an awareness that the - sic problem was common to many disciplines. This is not unreasonable: each of these disciplines has a large literature of its own, and a literature which is growing rapidly. Keeping up with any one of these is di?cult enough, let alone keeping up with others as well, which may in any case be couched in an - familiar technical language. But, of course, this means that opportunities are being lost, discoveries relating to the common problem made in one area are not transferred to the other area, and breakthroughs and problem solutions are being rediscovered, or not discovered for a long time, meaning that e?ort is being wasted and opportunities may be lost. 410 0$aLecture Notes in Artificial Intelligence ;$v2447 606 $aDatabase management 606 $aArtificial intelligence 606 $aAlgorithms 606 $aData structures (Computer science) 606 $aMathematical statistics 606 $aInformation storage and retrieval 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aData Structures and Information Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/I15009 606 $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 615 0$aDatabase management. 615 0$aArtificial intelligence. 615 0$aAlgorithms. 615 0$aData structures (Computer science). 615 0$aMathematical statistics. 615 0$aInformation storage and retrieval. 615 14$aDatabase Management. 615 24$aArtificial Intelligence. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aData Structures and Information Theory. 615 24$aProbability and Statistics in Computer Science. 615 24$aInformation Storage and Retrieval. 676 $a006.4 702 $aHand$b David J$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAdams$b Niall, M$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBolton$b Richard J$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aESF Exploratory Workshop 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465439103316 996 $aPattern Detection and Discovery$92065529 997 $aUNISA