A new approach to the issue of data quality in pattern recognition
Detailing foundational concepts before introducing more complex
methodologies and algorithms, this book is a self-contained manual for
advanced data analysis and data mining. Top-down organization presents
detailed applications only after methodological issues have been
mastered, and step-by-step instructions help ensure successful
implementation of new processes. By positioning data quality as a factor
to be dealt with rather than overcome, the framework provided serves as
a valuable, versatile tool in the analysis arsenal.
For decades, practical need has inspired intense theoretical and applied
research into pattern recognition for numerous and diverse applications.
Throughout, the limiting factor and perpetual problem has been data--its
sheer diversity, abundance, and variable quality presents the central
challenge to pattern recognition innovation. Pattern Recognition: A
Quality of Data Perspective repositions that challenge from a hurdle to
a given, and presents a new framework for comprehensive data analysis
that is designed specifically to accommodate problem data.
Designed as both a practical manual and a discussion about the most
useful elements of pattern recognition innovation, this book:
- Details fundamental pattern recognition concepts, including feature
space construction, classifiers, rejection, and evaluation
- Provides a systematic examination of the concepts, design methodology,
and algorithms involved in pattern recognition
- Includes numerous experiments, detailed schemes, and more advanced
problems that reinforce complex concepts
- Acts as a self-contained primer toward advanced solutions, with
detailed background and step-by-step processes
- Introduces the concept of granules and provides a framework for
granular computing
Pattern recognition plays a pivotal role in data analysis and data
mining, fields which are themselves being applied in an expanding sphere
of utility. By facing the data quality issue head-on, this book provides
students, practitioners, and researchers with a clear way forward amidst
the ever-expanding data supply.
