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Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series) - Hardcover
Synopsis
Unstructured Mining Approaches to Solve Complex Scientific Problems
As the volume of scientific data and literature increases exponentially, scientists need more powerful tools and methods to process and synthesize information and to formulate new hypotheses that are most likely to be both true and important. Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation describes a novel approach to scientific research that uses unstructured data analysis as a generative tool for new hypotheses.
The author develops a systematic process for leveraging heterogeneous structured and unstructured data sources, data mining, and computational architectures to make the discovery process faster and more effective. This process accelerates human creativity by allowing scientists and inventors to more readily analyze and comprehend the space of possibilities, compare alternatives, and discover entirely new approaches.
Encompassing systematic and practical perspectives, the book provides the necessary motivation and strategies as well as a heterogeneous set of comprehensive, illustrative examples. It reveals the importance of heterogeneous data analytics in aiding scientific discoveries and furthers data science as a discipline.
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About the Author
Scott Spangler is a principal data scientist, distinguished engineer, and master inventor in the Watson Innovations Group at the IBM Almaden Research Center. He has been involved with knowledge base and data mining research for the past 25 years. His recent work has applied Watson technology to help accelerate cancer research. He holds 45 patents and is the author of over 30 publications. He received a BS in mathematics from MIT and an MS in computer science from the University of Texas.
"About this title" may belong to another edition of this title.
- PublisherChapman and Hall/CRC
- Publication date2015
- ISBN 10 1482239132
- ISBN 13 9781482239133
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages270
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Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation
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CRC Press: Taylor & Francis Group, Boca Raton, FL, 2016
ISBN 10: 1482239132
ISBN 13: 9781482239133
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Hardcover. Condition: Fine. First Edition. Chapman & Hall/CRC Data Mining and Knowledge Discovery Series. xxi, preface, ack, b/w and color figures, 23 chapters, references, index, 270 pages. Clean cover, sound spine, unmarked interior pages. Size: 9.5 x 6.4 x .75: inches. Quantity Available: 1. Category: Science & Technology; ISBN/EAN: 9781482239133. Pictures of this item not already displayed here available upon request. Inventory No: 5713. Seller Inventory # 5713
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Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation
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Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
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Condition: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions. Seller Inventory # Z1-G-037-02015
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Accelerating Discovery
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Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation
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Accelerating Discovery: Mining Unstructured Information for Hypothesis Generation
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ISBN 10: 1482239132
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Scott Spangler is a principal data scientist, distinguished engineer, and master inventor in the Watson Innovations Group at the IBM Almaden Research Center. He has been involved with knowledge base and data mining research for the past . Seller Inventory # 32577424
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