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.
About the Author: WLADYSLAW HOMENDA, MSc., PhD, DSc., is an Associate Professor with the Faculty of Mathematics and Information Science at the Warsaw University of Technology, Poland, and an Associate Professor with the Faculty of Economics and Informatics in Vilnius at the University of Bialystok, Lithuania.
WITOLD PEDRYCZ is a Professor with the Systems Research Institute, Polish Academy of Sciences Warsaw, Poland and Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB, Canada.
