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Advances in Big Data Analytics : Theory, Algorithms and Practices / by Yong Shi.
SpringerLink Books Computer Science (2011-2024) Available online
SpringerLink Books Computer Science (2011-2024)- Format:
- Book
- Author/Creator:
- Shi, Yong, Author.
- Series:
- Computer Science (SpringerNature-11645)
- Language:
- English
- Subjects (All):
- Artificial intelligence-Data processing.
- Big data.
- Data mining.
- Computer science.
- Data Science.
- Big Data.
- Data Mining and Knowledge Discovery.
- Models of Computation.
- Local Subjects:
- Data Science.
- Big Data.
- Data Mining and Knowledge Discovery.
- Models of Computation.
- Physical Description:
- 1 online resource (XIV, 728 pages) : 1 illustrations
- Edition:
- 1st ed. 2022.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Singapore : Springer Nature Singapore : Imprint: Springer, 2022.
- System Details:
- text file PDF
- Summary:
- Today, 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.
- Contents:
- Part 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.
- Other Format:
- Printed edition:
- ISBN:
- 978-981-16-3607-3
- 9789811636073
- Access Restriction:
- Restricted for use by site license.
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