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Mining Sequential Patterns from Large Data Sets

AUTHOR Wang, Wei; Yang, Jiong
PUBLISHER Springer (12/06/2010)
PRODUCT TYPE Paperback (Paperback)

Description

In many applications, such as bioinformatics, web access traces, and system utilization logs, the data is naturally in the form of sequences. 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. It 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.

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Product Format
Product Details
ISBN-13: 9781441937070
ISBN-10: 1441937072
Binding: Paperback or Softback (Trade Paperback (Us))
Content Language: English
More Product Details
Page Count: 163
Carton Quantity: 48
Product Dimensions: 6.14 x 0.38 x 9.21 inches
Weight: 0.57 pound(s)
Country of Origin: NL
Subject Information
BISAC Categories
Computers | Data Science - Data Analytics
Computers | Computer Science
Computers | System Administration - Storage & Retrieval
Dewey Decimal: 006.3
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publisher marketing

In many applications, such as bioinformatics, web access traces, and system utilization logs, the data is naturally in the form of sequences. 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. It 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.

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Paperback