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007 cr nn 008mamaa
008 121227s2004 gw | s |||| 0|eng d
020 _a9783540444978
_9978-3-540-44497-8
024 7 _a10.1007/b99016
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
245 1 0 _aDatabase Support for Data Mining Applications
_h[electronic resource] :
_bDiscovering Knowledge with Inductive Queries /
_cedited by Rosa Meo, Pier L. Lanzi, Mika Klemettinen.
250 _a1st ed. 2004.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2004.
300 _aXII, 332 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 ;
_v2682
505 0 _aDatabase Languages and Query Execution -- Inductive Databases and Multiple Uses of Frequent Itemsets: The cInQ Approach -- Query Languages Supporting Descriptive Rule Mining: A Comparative Study -- Declarative Data Mining Using SQL3 -- Towards a Logic Query Language for Data Mining -- A Data Mining Query Language for Knowledge Discovery in a Geographical Information System -- Towards Query Evaluation in Inductive Databases Using Version Spaces -- The GUHA Method, Data Preprocessing and Mining -- Constraint Based Mining of First Order Sequences in SeqLog -- Support for KDD-Process -- Interactivity, Scalability and Resource Control for Efficient KDD Support in DBMS -- Frequent Itemset Discovery with SQL Using Universal Quantification -- Deducing Bounds on the Support of Itemsets -- Model-Independent Bounding of the Supports of Boolean Formulae in Binary Data -- Condensed Representations for Sets of Mining Queries -- One-Sided Instance-Based Boundary Sets -- Domain Structures in Filtering Irrelevant Frequent Patterns -- Integrity Constraints over Association Rules.
520 _aData mining from traditional relational databases as well as from non-traditional ones such as semi-structured data, Web data, and scientific databases housing biological, linguistic, and sensor data has recently become a popular way of discovering hidden knowledge. This book on database support for data mining is developed to approaches exploiting the available database technology, declarative data mining, intelligent querying, and associated issues, such as optimization, indexing, query processing, languages, and constraints. Attention is also paid to the solution of data preprocessing problems, such as data cleaning, discretization, and sampling. The 16 reviewed full papers presented were carefully selected from various workshops and conferences to provide complete and competent coverage of the core issues. Some papers were developed within an EC funded project on discovering knowledge with inductive queries.
650 0 _aArtificial intelligence.
650 0 _aDatabase management.
650 0 _aInformation storage and retrieval systems.
650 1 4 _aArtificial Intelligence.
650 2 4 _aDatabase Management.
650 2 4 _aInformation Storage and Retrieval.
700 1 _aMeo, Rosa.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aLanzi, Pier L.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aKlemettinen, Mika.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540224792
776 0 8 _iPrinted edition:
_z9783662188934
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2682
856 4 0 _uhttps://doi.org/10.1007/b99016
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
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