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Books > Computing & IT > Applications of computing > Artificial intelligence
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Mining Sequential Patterns from Large Data Sets (Hardcover, 2005 ed.)
Loot Price: R2,743
Discovery Miles 27 430
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Mining Sequential Patterns from Large Data Sets (Hardcover, 2005 ed.)
Series: Advances in Database Systems, 28
Expected to ship within 18 - 22 working days
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In many applications, e.g., bioinformatics, web access traces,
system u- lization logs, etc., the data is naturally in the form of
sequences. It has been of great interests to analyze the sequential
data to find their inherent char- teristics. The sequential pattern
is one of the most widely studied models to capture such
characteristics. Examples of sequential patterns include but are
not limited to protein sequence motifs and web page navigation
traces. In this book, we focus on sequential pattern mining. To
meet different needs of various applications, several models of
sequential patterns have been proposed. We do not only study the
mathematical definitions and application domains of these models,
but also the algorithms on how to effectively and efficiently find
these patterns. The objective of this book is to provide computer
scientists and domain - perts such as life scientists with a set of
tools in analyzing and understanding the nature of various
sequences by : (1) identifying the specific model(s) of - quential
patterns that are most suitable, and (2) providing an efficient
algorithm for mining these patterns. Chapter 1 INTRODUCTION Data
Mining is the process of extracting implicit knowledge and
discovery of interesting characteristics and patterns that are not
explicitly represented in the databases. The techniques can play an
important role in understanding data and in capturing intrinsic
relationships among data instances. Data mining has been an active
research area in the past decade and has been proved to be very
useful.
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