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Reseña del editor
Data mining provides a set of new techniques to integrate, synthesize, and analyze tdata, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization.
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One of the major challenges for the scientific community, a challenge that has been seen in many business disciplines, is the exponential increase in data being generated by new experimental techniques and research. A single microarray experiment, for example, can generate thousands of data points that need to be analyzed, and this problem is predicted to increase. As new techniques in areas such as genomics and proteomics continue to be adopted into the mainstream as the costs fall, the need for effective mechanisms for synthesizing these disparate forms of data together for analysis is of paramount importance. But the sheer volume of data means that traditional techniques need to be augmented by approaches that elicit knowledge from the data, using automated procedures.
Data mining provides a set of such techniques, new techniques to integrate, synthesize, and analyze the data, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization."Sobre este título" puede pertenecer a otra edición de este libro.
- EditorialHumana
- Año de publicación2014
- ISBN 10 1627039481
- ISBN 13 9781627039482
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas656
EUR 294,07
EUR 29,37 gastos de envío desde Reino Unido a Estados Unidos de America
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EUR 210,31
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Introduction to Data Mining for the Life Sciences
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Introduction to Data Mining for the Life Sciences
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data mining provides a set of new techniques to integrate, synthesize, and analyze tdata, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization. 656 pp. Englisch. Nº de ref. del artículo: 9781627039482
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Introduction to Data Mining for the Life Sciences
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Data mining involves uncovering the patterns inherent within the data Provides a set of techniques that can help the life scientist leverage the valuable data asset With each set of techniques are tangible examples to support themDa. Nº de ref. del artículo: 4267803
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Introduction to Data Mining for the Life Sciences
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Humana Press, Humana Press, 2014
ISBN 10: 1627039481
ISBN 13: 9781627039482
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data mining provides a set of new techniques to integrate, synthesize, and analyze tdata, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization. Nº de ref. del artículo: 9781627039482
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Introduction to Data Mining for the Life Sciences
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Introduction to Data Mining for the Life Sciences
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Introduction to Data Mining for the Life Sciences
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