000 | 03140nam a22005295i 4500 | ||
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001 | 978-1-4939-0539-3 | ||
003 | DE-He213 | ||
005 | 20200420220226.0 | ||
007 | cr nn 008mamaa | ||
008 | 140214s2014 xxu| s |||| 0|eng d | ||
020 |
_a9781493905393 _9978-1-4939-0539-3 |
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024 | 7 |
_a10.1007/978-1-4939-0539-3 _2doi |
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050 | 4 | _aQA76.9.D343 | |
072 | 7 |
_aUNF _2bicssc |
|
072 | 7 |
_aUYQE _2bicssc |
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072 | 7 |
_aCOM021030 _2bisacsh |
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082 | 0 | 4 |
_a006.312 _223 |
100 | 1 |
_aDahan, Haim. _eauthor. |
|
245 | 1 | 0 |
_aProactive Data Mining with Decision Trees _h[electronic resource] / _cby Haim Dahan, Shahar Cohen, Lior Rokach, Oded Maimon. |
264 | 1 |
_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2014. |
|
300 |
_aX, 88 p. 20 illus. _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 |
||
490 | 1 |
_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112 |
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505 | 0 | _aIntroduction -- Proactive Data Mining: A General Approach -- Proactive Data Mining Using Decision Trees -- Proactive Data Mining in the Real World: Case Studies -- Sensitivity Analysis of Proactive Data Mining -- Conclusions. | |
520 | _aThis book explores a proactive and domain-driven method to classification tasks. This novel proactive approach to data mining not only induces a model for predicting or explaining a phenomenon, but also utilizes specific problem/domain knowledge to suggest specific actions to achieve optimal changes in the value of the target attribute. In particular, the authors suggest a specific implementation of the domain-driven proactive approach for classification trees. The book centers on the core idea of moving observations from one branch of the tree to another. It introduces a novel splitting criterion for decision trees, termed maximal-utility, which maximizes the potential for enhancing profitability in the output tree. Two real-world case studies, one of a leading wireless operator and the other of a major security company, are also included and demonstrate how applying the proactive approach to classification tasks can solve business problems. Proactive Data Mining with Decision Trees is intended for researchers, practitioners and advanced-level students. | ||
650 | 0 | _aComputer science. | |
650 | 0 | _aData mining. | |
650 | 0 | _aInformation storage and retrieval. | |
650 | 1 | 4 | _aComputer Science. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aInformation Storage and Retrieval. |
650 | 2 | 4 | _aInformation Systems Applications (incl. Internet). |
700 | 1 |
_aCohen, Shahar. _eauthor. |
|
700 | 1 |
_aRokach, Lior. _eauthor. |
|
700 | 1 |
_aMaimon, Oded. _eauthor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9781493905386 |
830 | 0 |
_aSpringerBriefs in Electrical and Computer Engineering, _x2191-8112 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-1-4939-0539-3 |
912 | _aZDB-2-SCS | ||
942 | _cEBK | ||
999 |
_c52231 _d52231 |