| 000 | 03179nam a2200529 i 4500 | ||
|---|---|---|---|
| 001 | 6267275 | ||
| 003 | IEEE | ||
| 005 | 20220712204617.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151228s2001 mau ob 001 eng d | ||
| 010 | _z 2001032620 (print) | ||
| 020 |
_a9780262256308 _qelectronic |
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| 020 |
_z9780262082907 _qhardcover |
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| 020 |
_z026208290X _qhc. : alk. paper |
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| 035 | _a(CaBNVSL)mat06267275 | ||
| 035 | _a(IDAMS)0b000064818b4261 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aQA76.9.D343 _bH38 2001eb |
|
| 082 | 0 | 0 |
_a006.3 _221 |
| 100 | 1 |
_aHand, D. J., _eauthor. _921880 |
|
| 245 | 1 | 0 |
_aPrinciples of data mining / _cDavid Hand, Heikki Mannila, Padhraic Smyth. |
| 264 | 1 |
_aCambridge, Massachusetts : _bMIT Press, _c2001. |
|
| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2001] |
|
| 300 | _a1 PDF (xxxii, 546 pages). | ||
| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 | _aAdaptive computation and machine learning series | |
| 500 | _a"A Bradford book." | ||
| 504 | _aIncludes bibliographical references (p. [491]-524) and index. | ||
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aThe growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/28/2015. | ||
| 650 | 0 |
_aData mining. _93907 |
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| 655 | 0 |
_aElectronic books. _93294 |
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| 700 | 1 |
_aMannila, Heikki. _921881 |
|
| 700 | 1 |
_aSmyth, Padhraic. _921882 |
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| 710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _921883 |
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| 710 | 2 |
_aMIT Press, _epublisher. _921884 |
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| 776 | 0 | 8 |
_iPrint version _z9780262082907 |
| 830 | 0 |
_aAdaptive computation and machine learning series _921885 |
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| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267275 |
| 942 | _cEBK | ||
| 999 |
_c72933 _d72933 |
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