| Sequence Data Mining 2007 Edition Contributor(s): Dong, Guozhu (Author), Pei, Jian (Author) |
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ISBN: 0387699368 ISBN-13: 9780387699363 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: August 2007 Annotation: 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.
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| Additional Information |
| BISAC Categories: - Computers | Databases - Data Mining - Computers | Networking - General - Computers | System Administration - Storage & Retrieval |
| Dewey: 006.3 |
| LCCN: 2007927815 |
| Series: Advances in Database Systems |
| Physical Information: 0.44" H x 6.14" W x 9.21" (0.91 lbs) 150 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |