Mining Sequential Patterns from Large Data Sets
By: Wang, Wei [author.].
Contributor(s): Yang, Jiong [author.] | SpringerLink (Online service).
Material type:
BookSeries: Advances in Database Systems: 28Publisher: Boston, MA : Springer US, 2005.Description: XV, 163 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9780387242477.Subject(s): Computer science | Computer communication systems | Data structures (Computer science) | Database management | Data mining | Information storage and retrieval | Multimedia information systems | Computer Science | Data Mining and Knowledge Discovery | Database Management | Information Storage and Retrieval | Data Structures | Multimedia Information Systems | Computer Communication NetworksDDC classification: 006.312 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK85 |
Related Work -- Periodic Patterns -- Statistically Significant Patterns -- Approximate Patterns -- Conclusion Remark.
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. .
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