Sequence Data Mining
(Sprache: Englisch)
This book provides balanced coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods.
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This book provides balanced coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods.
Klappentext zu „Sequence Data Mining “
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.
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 includes: DNA, protein, customer purchase history, web surfing history; and more.
Sequence Data Mining provides a 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 which allows readers to access the state-of-the-art results in one place.
Sequence Data Mining will be useful to professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for graduate-level students in computer science and bioengineering.
Sequence Data Mining provides a 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 which allows readers to access the state-of-the-art results in one place.
Sequence Data Mining will be useful to professionals working in bioinformatics, genomics, web services, and financial data analysis. This book is also suitable for graduate-level students in computer science and bioengineering.
Inhaltsverzeichnis zu „Sequence Data Mining “
- Preface- Introduction
- Frequent/closed sequence patterns
- Similarity sequence patterns and motifs
- Markov Models and Hidden Markov Models
- Sequence classification and feature selection
- Sequential (partial) periodicity
- Partial task ordering discovery
- Rare event and distinguishing patterns
- Related topics
- Index
Autoren-Porträt von Guozhu Dong, Jian Pei
Jian Pei is Associate Professor of Computing Science and the director of Collaborative Research and Industry Relations at the School of Computing Science at Simon Fraser University, Canada. In 2002-2004, he was an Assistant Professor of Computer Science and Engineering at the State University of New York (SUNY) at Buffalo. He received a Ph.D. degree in Computing Science from Simon Fraser University in 2002, under Dr. Jiawei Han's supervision.
Bibliographische Angaben
- Autoren: Guozhu Dong , Jian Pei
- 2007, 150 Seiten, 20 Abbildungen, Maße: 23,4 x 15,6 cm, Gebunden, Englisch
- Verlag: Springer
- ISBN-10: 0387699368
- ISBN-13: 9780387699363
Sprache:
Englisch
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