Accelerating Discovery : Mining Unstructured Information for Hypothesis Generation Hardback
by Scott (IBM Research, San Jose, California, USA) Spangler
Part of the Chapman & Hall/CRC Data Mining and Knowledge Discovery Series series
Hardback
Description
Unstructured Mining Approaches to Solve Complex Scientific ProblemsAs 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.
Information
-
Out of stock
- Format:Hardback
- Pages:270 pages, 122 Illustrations, black and white
- Publisher:Taylor & Francis Inc
- Publication Date:09/10/2015
- Category:
- ISBN:9781482239133
Information
-
Out of stock
- Format:Hardback
- Pages:270 pages, 122 Illustrations, black and white
- Publisher:Taylor & Francis Inc
- Publication Date:09/10/2015
- Category:
- ISBN:9781482239133
