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Accelerating Discovery : Mining Unstructured Information for Hypothesis Generation - Scott Spangler

Accelerating Discovery

Mining Unstructured Information for Hypothesis Generation

By: Scott Spangler

eText | 18 September 2015 | Edition Number 1

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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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You Can Find This eBook In

Non-FictionEconomicsEconometricsEconomic StatisticsComputing & I.T.Computer ScienceDigital Lifestyle & Online World: Consumer & User GuidesComputer Games & Online Games Strategy GuidesDatabasesData MiningEngineering & TechnologyElectronics & Communications Engineering
Electronics EngineeringAutomatic Control EngineeringMathematicsProbability & Statistics

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Published: 30th June 2020

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