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Sequence Data Mining [electronic resource] / by Guozhu Dong, Jian Pei.

Por: Dong, Guozhu [author.]Colaborador(es): Pei, Jian [author.]Tipo de material: TextoTextoSeries Advances in Database Systems, 33Editor: Boston, MA : Springer US, 2007Descripción: XVI, 150 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9780387699370Trabajos contenidos: SpringerLink (Online service)Tema(s): Computer science | Computer Communication Networks | Database management | Data mining | Information storage and retrieval systems | Biometrics | Bioinformatics | Computer Science | Data Mining and Knowledge Discovery | Information Storage and Retrieval | Database Management | Computational Biology/Bioinformatics | Biometrics | Computer Communication NetworksFormatos físicos adicionales: Sin títuloClasificación CDD: 006.312 Clasificación LoC:QA76.9.D343Recursos en línea: de clik aquí para ver el libro electrónico
Contenidos:
Springer eBooksResumen: 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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Frequent and Closed Sequence Patterns -- Classification, Clustering, Features and Distances of Sequence Data -- Sequence Motifs: Identifying and Characterizing Sequence Families -- Mining Partial Orders from Sequences -- Distinguishing Sequence Patterns -- Related Topics.

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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