Advances in Big Data Analytics

Theory, Algorithms and Practices

AvYong Shi

Inbunden, Engelska, 2022

1 890 kr

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Häftad

1 890 kr

Beskrivning

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.

Produktinformation

Utgivningsdatum:2022-01-14
Mått:155 x 235 x 46 mm
Vikt:1 267 g
Format:Inbunden
Språk:Engelska
Antal sidor:728
Förlag:Springer Verlag, Singapore
ISBN:9789811636066

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Databaser inom Data och IT

Mer om författaren

Yong Shi is the Director of the Research Center on Fictitious Economy and Data Science, and Director of the Key Lab of Big Data Mining and Knowledge Management, Chinese Academy of Sciences. He has been an Isaacson Professor, Union Pacific Chair, and Charles W. and Margre H. Durham Distinguished Professor of Information Technology at the College of Information Science and Technology, University of Nebraska at Omaha, USA. He has served on the State Council of the PRC (2016), as an elected member of the International Eurasian Academy of Science (2017), and as an elected fellow of the World Academy of Sciences for the Advancement of Science in Developing Countries (2015). His research interests include big data analysis, data science, business intelligence, data mining and multiple-criteria decision making. He has published more than 20 books, over 500 papers in various journals, and numerous conferences/proceedings papers. He is the Editor-in-Chief of both the International Journal ofInformation Technology and Decision Making (SCI) and of Annals of Data Science (Springer), and serves on the Editorial Boards of numerous academic journals.

Innehållsförteckning

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.