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Mining Sequential Patterns from Large Data Sets
The focus of Mining Sequential Patterns from Large Data Sets is on sequential pattern mining. In many applications, such as bioinformatics, web access traces, system utilization logs, etc., the data is naturally in the form of sequences. This information has been of great interest for analyzing the...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer US : Imprint: Springer,
2005.
|
| Edición: | 1st ed. 2005. |
| Colección: | Advances in Database Systems ;
28 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 245 | 1 | 0 | |a Mining Sequential Patterns from Large Data Sets |h [electronic resource] / |c by Wei Wang, Jiong Yang. |
| 250 | |a 1st ed. 2005. | ||
| 264 | 1 | |a New York, NY : |b Springer US : |b Imprint: Springer, |c 2005. | |
| 300 | |a XV, 163 p. |b online resource. | ||
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| 490 | 1 | |a Advances in Database Systems ; |v 28 | |
| 505 | 0 | |a Related Work -- Periodic Patterns -- Statistically Significant Patterns -- Approximate Patterns -- Conclusion Remark. | |
| 520 | |a The focus of Mining Sequential Patterns from Large Data Sets is on sequential pattern mining. In many applications, such as bioinformatics, web access traces, system utilization logs, etc., the data is naturally in the form of sequences. This information has been of great interest for analyzing the sequential data to find its inherent characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. To meet the different needs of various applications, several models of sequential patterns have been proposed. This volume not only studies the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. Mining Sequential Patterns from Large Data Sets provides a set of tools for analyzing and understanding the nature of various sequences by identifying the specific model(s) of sequential patterns that are most suitable. This book provides an efficient algorithm for mining these patterns. Mining Sequential Patterns from Large Data Sets is designed for a professional audience of researchers and practitioners in industry and also suitable for graduate-level students in computer science. . | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Database management. | |
| 650 | 0 | |a Information storage and retrieval systems. | |
| 650 | 0 | |a Artificial intelligence-Data processing. | |
| 650 | 0 | |a Multimedia systems. | |
| 650 | 0 | |a Computer networks . | |
| 650 | 1 | 4 | |a Data Mining and Knowledge Discovery. |
| 650 | 2 | 4 | |a Database Management. |
| 650 | 2 | 4 | |a Information Storage and Retrieval. |
| 650 | 2 | 4 | |a Data Science. |
| 650 | 2 | 4 | |a Multimedia Information Systems. |
| 650 | 2 | 4 | |a Computer Communication Networks. |
| 700 | 1 | |a Yang, Jiong. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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| 776 | 0 | 8 | |i Printed edition: |z 9781441937070 |
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| 830 | 0 | |a Advances in Database Systems ; |v 28 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/b104937 |z Texto Completo |
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| 950 | |a Computer Science (SpringerNature-11645) | ||
| 950 | |a Computer Science (R0) (SpringerNature-43710) | ||