TY  - BOOK
AU  - 
AU  - 
ED  - SpringerLink (Online service)
TI  - Mining Sequential Patterns from Large Data Sets
T2  - Advances in Database Systems,
SN  - 9780387242477
AV  - QA76.9.D343 
U1  - 006.312 23
PY  - 2005///
CY  - Boston, MA
PB  - Springer US
KW  - Computer science
KW  - Computer Communication Networks
KW  - Data structures (Computer science)
KW  - Database management
KW  - Data mining
KW  - Information storage and retrieval systems
KW  - Multimedia systems
KW  - Computer Science
KW  - Data Mining and Knowledge Discovery
KW  - Database Management
KW  - Information Storage and Retrieval
KW  - Data Structures
KW  - Multimedia Information Systems
N1  - Related Work -- Periodic Patterns -- Statistically Significant Patterns -- Approximate Patterns -- Conclusion Remark; ZDB-2-SCS
N2  - 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
UR  - http://dx.doi.org/10.1007/b104937
ER  -