Mining Sequential Patterns from Large Data Sets

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Mining Sequential Patterns from Large Data Sets

Wang, Wei. - Nama Orang; Yang, Jiong. - Nama Orang;

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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Judul Seri: -
No. Panggil: -
Penerbit: Boston : Springer., 2005
Deskripsi Fisik: XV, 163 p.online resource.
Bahasa: English
ISBN/ISSN: 9780387242477
Klasifikasi: 006.312
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: 1st ed.
Subjek: Computer communication systems.
Computer Communication Networks.
Data mining.
Data Mining and Knowledge Discovery.
Information storage and retrieval.
Data structures (Computer science).
Data Structures.
Database management.
Multimedia information systems.
Info Detail Spesifik: -
Pernyataan Tanggungjawab: Wei Wang, Jiong Yang.

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