000 05106nam a22006735i 4500
001 978-3-540-45728-2
003 DE-He213
005 20240423132431.0
007 cr nn 008mamaa
008 121227s2002 gw | s |||| 0|eng d
020 _a9783540457282
_9978-3-540-45728-2
024 7 _a10.1007/3-540-45728-3
_2doi
050 4 _aQA76.9.D3
072 7 _aUN
_2bicssc
072 7 _aCOM021000
_2bisacsh
072 7 _aUN
_2thema
082 0 4 _a005.74
_223
245 1 0 _aPattern Detection and Discovery
_h[electronic resource] :
_bESF Exploratory Workshop, London, UK, September 16-19, 2002. /
_cedited by David J Hand, Niall, M. Adams, Richard J. Bolton.
250 _a1st ed. 2002.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2002.
300 _aXII, 232 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2447
505 0 _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.
520 _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.
650 0 _aDatabase management.
650 0 _aArtificial intelligence.
650 0 _aAlgorithms.
650 0 _aData structures (Computer science).
650 0 _aInformation theory.
650 0 _aComputer science
_xMathematics.
650 0 _aMathematical statistics.
650 0 _aInformation storage and retrieval systems.
650 1 4 _aDatabase Management.
650 2 4 _aArtificial Intelligence.
650 2 4 _aAlgorithms.
650 2 4 _aData Structures and Information Theory.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aInformation Storage and Retrieval.
700 1 _aHand, David J.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aAdams, Niall, M.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aBolton, Richard J.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540441489
776 0 8 _iPrinted edition:
_z9783662199459
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2447
856 4 0 _uhttps://doi.org/10.1007/3-540-45728-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
912 _aZDB-2-BAE
942 _cSPRINGER
999 _c187714
_d187714