Sequence Data Mining (Paperback)

Guozhu Dong

ISBN 10: 1441943528 / ISBN 13: 9781441943521

Editore: Springer-Verlag New York Inc., New York, NY, 2010

Riguardo questo articolo

Paperback. Understanding sequence data, and the ability to utilize this hidden knowledge, will create a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. This book provides thorough coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. It offers balanced coverage on data mining and sequence data analysis, allowing readers to access the state-of-the-art results in one place. This book provides balanced coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Codice articolo 9781441943521

Dati bibliografici

• Titolo: Sequence Data Mining (Paperback)
• Editore: Springer-Verlag New York Inc., New York, NY
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 1441943528
• ISBN 13: 9781441943521
• Rilegatura: Paperback
• Edizione: prima edizione
• Condizione: new

Descrizione articolo

Riassunto

This book provides balanced coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This volume fills in the gap, allowing readers to access the state-of-the-art results in one place. Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering.

Dalla quarta di copertina

Understanding sequence data, and the ability to utilize this hidden knowledge, creates a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more.

Sequence Data Mining provides balanced coverage of the existing results on sequence data mining, as well as pattern types and associated pattern mining methods. While there are several books on data mining and sequence data analysis, currently there are no books that balance both of these topics. This professional volume fills in the gap, allowing readers to access state-of-the-art results in one place.

Sequence Data Mining is designed for professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for advanced-level students in computer science and bioengineering.

Forward by Professor Jiawei Han, University of Illinois at Urbana-Champaign.