000 | 03478nam a22006015i 4500 | ||
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001 | 978-3-540-75549-4 | ||
003 | DE-He213 | ||
005 | 20240730184944.0 | ||
007 | cr nn 008mamaa | ||
008 | 100301s2007 gw | s |||| 0|eng d | ||
020 |
_a9783540755494 _9978-3-540-75549-4 |
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024 | 7 |
_a10.1007/978-3-540-75549-4 _2doi |
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050 | 4 | _aQA76.9.D35 | |
050 | 4 | _aQ350-390 | |
072 | 7 |
_aUMB _2bicssc |
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_a005.73 _223 |
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245 | 1 | 0 |
_aKnowledge Discovery in Inductive Databases _h[electronic resource] : _b5th International Workshop, KDID 2006 Berlin, Germany, September 18th, 2006 Revised Selected and Invited Papers / _cedited by Saso Dzeroski, Jan Struyf. |
250 | _a1st ed. 2007. | ||
264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg : _bImprint: Springer, _c2007. |
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300 |
_aX, 301 p. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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490 | 1 |
_aInformation Systems and Applications, incl. Internet/Web, and HCI, _x2946-1642 ; _v4747 |
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505 | 0 | _aInvited Talk -- Value, Cost, and Sharing: Open Issues in Constrained Clustering -- Contributed Papers -- Mining Bi-sets in Numerical Data -- Extending the Soft Constraint Based Mining Paradigm -- On Interactive Pattern Mining from Relational Databases -- Analysis of Time Series Data with Predictive Clustering Trees -- Integrating Decision Tree Learning into Inductive Databases -- Using a Reinforced Concept Lattice to Incrementally Mine Association Rules from Closed Itemsets -- An Integrated Multi-task Inductive Database VINLEN: Initial Implementation and Early Results -- Beam Search Induction and Similarity Constraints for Predictive Clustering Trees -- Frequent Pattern Mining and Knowledge Indexing Based on Zero-Suppressed BDDs -- Extracting Trees of Quantitative Serial Episodes -- IQL: A Proposal for an Inductive Query Language -- Mining Correct Properties in Incomplete Databases -- Efficient Mining Under Rich Constraints Derived from Various Datasets -- Three Strategies for Concurrent Processing of Frequent Itemset Queries Using FP-Growth -- Discussion Paper -- Towards a General Framework for Data Mining. | |
650 | 0 |
_aData structures (Computer science). _98188 |
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650 | 0 |
_aInformation theory. _914256 |
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650 | 0 |
_aDatabase management. _93157 |
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650 | 0 |
_aArtificial intelligence. _93407 |
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650 | 1 | 4 |
_aData Structures and Information Theory. _931923 |
650 | 2 | 4 |
_aDatabase Management. _93157 |
650 | 2 | 4 |
_aArtificial Intelligence. _93407 |
700 | 1 |
_aDzeroski, Saso. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9137366 |
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700 | 1 |
_aStruyf, Jan. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt _9137367 |
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710 | 2 |
_aSpringerLink (Online service) _9137368 |
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773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783540755487 |
776 | 0 | 8 |
_iPrinted edition: _z9783540844846 |
830 | 0 |
_aInformation Systems and Applications, incl. Internet/Web, and HCI, _x2946-1642 ; _v4747 _9137369 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-540-75549-4 |
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