000 | 02935nam a22004935i 4500 | ||
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001 | 978-3-540-75549-4 | ||
003 | DE-He213 | ||
005 | 20160624102113.0 | ||
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.D3 | |
072 | 7 |
_aUN _2bicssc |
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072 | 7 |
_aUMT _2bicssc |
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072 | 7 |
_aCOM021000 _2bisacsh |
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082 | 0 | 4 |
_a005.74 _223 |
245 | 1 | 0 |
_aKnowledge Discovery in Inductive Databases _h[electronic resource] : _b5th International Workshop, KDID 2006 Berlin, Germany, September 18, 2006 Revised Selected and Invited Papers / _cedited by Sašo Džeroski, Jan Struyf. |
260 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2007. |
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264 | 1 |
_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2007. |
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300 |
_aX, 301 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 Computer Science, _x0302-9743 ; _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 | _aComputer science. | |
650 | 0 | _aDatabase management. | |
650 | 0 | _aArtificial intelligence. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aDatabase Management. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
700 | 1 |
_aDžeroski, Sašo. _eeditor. |
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700 | 1 |
_aStruyf, Jan. _eeditor. |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783540755487 |
786 | _dSpringer | ||
830 | 0 |
_aLecture Notes in Computer Science, _x0302-9743 ; _v4747 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-540-75549-4 |
942 |
_2EBK7874 _cEBK |
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999 |
_c37168 _d37168 |