| 000 | 05878nam a2201285 i 4500 | ||
|---|---|---|---|
| 001 | 5265979 | ||
| 003 | IEEE | ||
| 005 | 20220712205703.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 100317t20152003njua ob 001 0 eng d | ||
| 020 |
_a9780470544341 _qelectronic |
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| 020 |
_z9780470890455 _qprint |
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| 020 |
_z0470544341 _qelectronic |
||
| 024 | 7 |
_a10.1109/9780470544341 _2doi |
|
| 035 | _a(CaBNVSL)mat05265979 | ||
| 035 | _a(IDAMS)0b000064810c5b7f | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aQA76.9.D343 _bK36 2003eb |
|
| 082 | 0 | 4 |
_a006.3/12 _222 |
| 100 | 1 |
_aKantardzic, Mehmed, _eauthor. _927024 |
|
| 245 | 1 | 0 |
_aData mining : _bconcepts, models, methods, and algorithms / _cMehmed Kantardzic. |
| 264 | 1 |
_aHoboken, New Jersey : _bWiley-Interscience, _c2003. |
|
| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2009] |
|
| 300 |
_a1 PDF (xii, 345 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 504 | _aIncludes bibliographical references and index. | ||
| 505 | 0 | _aPreparing the Data -- Data Reduction -- Learning from Data -- Statistical Methods -- Cluster Analysis -- Decision Trees and Decision Rules -- Association Rules -- Artificial Neural Networks -- Genetic Algorithms -- Fuzzy Sets and Fuzzy Logic -- Visualization Methods -- Data-Mining Tools -- Data-Mining Applications. | |
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aA comprehensive introduction to the exploding field of data mining We are surrounded by data, numerical and otherwise, which must be analyzed and processed to convert it into information that informs, instructs, answers, or otherwise aids understanding and decision-making. Due to the ever-increasing complexity and size of today's data sets, a new term, data mining, was created to describe the indirect, automatic data analysis techniques that utilize more complex and sophisticated tools than those which analysts used in the past to do mere data analysis. Data Mining: Concepts, Models, Methods, and Algorithms discusses data mining principles and then describes representative state-of-the-art methods and algorithms originating from different disciplines such as statistics, machine learning, neural networks, fuzzy logic, and evolutionary computation. Detailed algorithms are provided with necessary explanations and illustrative examples. This text offers guidance: how and when to use a particular software tool (with their companion data sets) from among the hundreds offered when faced with a data set to mine. This allows analysts to create and perform their own data mining experiments using their knowledge of the methodologies and techniques provided. This book emphasizes the selection of appropriate methodologies and data analysis software, as well as parameter tuning. These critically important, qualitative decisions can only be made with the deeper understanding of parameter meaning and its role in the technique that is offered here. Data mining is an exploding field and this book offers much-needed guidance to selecting among the numerous analysis programs that are available. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/21/2015. | ||
| 650 | 0 |
_aData mining. _93907 |
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| 655 | 0 |
_aElectronic books. _93294 |
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| 695 | _aNeurons | ||
| 695 | _aOptical fiber cables | ||
| 695 | _aOptimization | ||
| 695 | _aParameter estimation | ||
| 695 | _aPartitioning algorithms | ||
| 695 | _aPredictive models | ||
| 695 | _aRain | ||
| 695 | _aSections | ||
| 695 | _aServers | ||
| 695 | _aShape | ||
| 695 | _aStatistical analysis | ||
| 695 | _aSystem identification | ||
| 695 | _aTelecommunications | ||
| 695 | _aTemperature measurement | ||
| 695 | _aThree dimensional displays | ||
| 695 | _aTraining | ||
| 695 | _aTraining data | ||
| 695 | _aVisualization | ||
| 695 | _aAccuracy | ||
| 695 | _aAdaptive systems | ||
| 695 | _aAlgorithm design and analysis | ||
| 695 | _aAnalytical models | ||
| 695 | _aApproximation algorithms | ||
| 695 | _aApproximation methods | ||
| 695 | _aArtificial neural networks | ||
| 695 | _aAssociation rules | ||
| 695 | _aBibliographies | ||
| 695 | _aBiographies | ||
| 695 | _aBiological cells | ||
| 695 | _aBusiness | ||
| 695 | _aCable TV | ||
| 695 | _aCities and towns | ||
| 695 | _aClassification algorithms | ||
| 695 | _aClassification tree analysis | ||
| 695 | _aClustering algorithms | ||
| 695 | _aCommunities | ||
| 695 | _aCompanies | ||
| 695 | _aComplexity theory | ||
| 695 | _aComputational modeling | ||
| 695 | _aData analysis | ||
| 695 | _aData mining | ||
| 695 | _aData models | ||
| 695 | _aData visualization | ||
| 695 | _aDatabases | ||
| 695 | _aDispersion | ||
| 695 | _aDistributed databases | ||
| 695 | _aEncoding | ||
| 695 | _aEstimation | ||
| 695 | _aFault tolerance | ||
| 695 | _aFuzzy logic | ||
| 695 | _aFuzzy sets | ||
| 695 | _aGallium | ||
| 695 | _aGenerators | ||
| 695 | _aGenetic algorithms | ||
| 695 | _aGenetics | ||
| 695 | _aHumans | ||
| 695 | _aHypercubes | ||
| 695 | _aImage color analysis | ||
| 695 | _aIndexes | ||
| 695 | _aIndustries | ||
| 695 | _aItemsets | ||
| 695 | _aLearning | ||
| 695 | _aLearning systems | ||
| 695 | _aLoss measurement | ||
| 695 | _aMachine learning | ||
| 695 | _aMathematical model | ||
| 695 | _aMeasurement uncertainty | ||
| 710 | 2 |
_aJohn Wiley & Sons, _epublisher. _96902 |
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| 710 | 2 |
_aIEEE Xplore (Online service), _edistributor. _927025 |
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| 776 | 0 | 8 |
_iPrint version: _z9780470890455 |
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=5265979 |
| 942 | _cEBK | ||
| 999 |
_c73945 _d73945 |
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