| 000 | 03661nam a22005415i 4500 | ||
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| 001 | 978-981-16-3607-3 | ||
| 003 | DE-He213 | ||
| 005 | 20240423125521.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 220113s2022 si | s |||| 0|eng d | ||
| 020 |
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| 024 | 7 |
_a10.1007/978-981-16-3607-3 _2doi |
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| 072 | 7 |
_aUN _2bicssc |
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_aCOM021000 _2bisacsh |
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_a005.7 _223 |
| 100 | 1 |
_aShi, Yong. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 245 | 1 | 0 |
_aAdvances in Big Data Analytics _h[electronic resource] : _bTheory, Algorithms and Practices / _cby Yong Shi. |
| 250 | _a1st ed. 2022. | ||
| 264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2022. |
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| 300 |
_aXIV, 728 p. 1 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 |
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| 505 | 0 | _aPart One: Concept and Theoretical Foundation -- Chapter 1: Big Data and Big Data Analytics -- Chapter 2: Multiple Criteria Optimization Classification -- Chapter 3: Support Vector Machine Classification -- Part Two: Functional Analysis -- Chapter 4: Feature Selection -- Chapter 5: Data Stream Analysis -- Chapter 6: Learning Analysis -- Chapter 7: Sentiment Analysis -- Chapter 8: Link Analysis -- Chapter 9: Evaluation Analysis -- Part Three: Application and Future Analysis -- Chapter 10: Business and Engineering Applications -- Chapter 11: Healthcare Applications -- Chapter 12: Artificial Intelligence IQ Test -- Chapter 13: Conclusions. | |
| 520 | _aToday, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence. Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems. | ||
| 650 | 0 |
_aArtificial intelligence _xData processing. |
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| 650 | 0 | _aBig data. | |
| 650 | 0 | _aData mining. | |
| 650 | 0 | _aComputer science. | |
| 650 | 1 | 4 | _aData Science. |
| 650 | 2 | 4 | _aBig Data. |
| 650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
| 650 | 2 | 4 | _aModels of Computation. |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811636066 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811636080 |
| 776 | 0 | 8 |
_iPrinted edition: _z9789811636097 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-16-3607-3 |
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| 912 | _aZDB-2-SXCS | ||
| 942 | _cSPRINGER | ||
| 999 |
_c178797 _d178797 |
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