000 | 04183nam a22005895i 4500 | ||
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001 | 978-3-540-44497-8 | ||
003 | DE-He213 | ||
005 | 20240423125551.0 | ||
007 | cr nn 008mamaa | ||
008 | 121227s2004 gw | s |||| 0|eng d | ||
020 |
_a9783540444978 _9978-3-540-44497-8 |
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024 | 7 |
_a10.1007/b99016 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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072 | 7 |
_aUYQ _2thema |
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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. |
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300 |
_aXII, 332 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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 |
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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 |
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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 | ||
912 | _aZDB-2-BAE | ||
942 | _cSPRINGER | ||
999 |
_c179368 _d179368 |