Buch, Englisch, 320 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 636 g
ISBN: 978-1-118-00440-1
Verlag: Wiley
Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection.
Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Next, the authors explore such topics and issues as:
* Soft computing in pattern recognition and data mining
* A Mathematical framework for generalized rough sets, incorporating the concept of fuzziness in defining the granules as well as the set
* Selection of non-redundant and relevant features of real-valued data sets
* Selection of the minimum set of basis strings with maximum information for amino acid sequence analysis
* Segmentation of brain MR images for visualization of human tissues
Numerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This text--covering the latest findings as well as directions for future research--is recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Foreword xiii
Preface xv
About the Authors xix
1 Introduction to Pattern Recognition and Data Mining 1
1.1 Introduction 1
1.2 Pattern Recognition 3
1.2.1 Data Acquisition 4
1.2.2 Feature Selection 4
1.2.3 Classification and Clustering 5
1.3 Data Mining 6
1.3.1 Tasks, Tools, and Applications 7
1.3.2 Pattern Recognition Perspective 8
1.4 Relevance of Soft Computing 9
1.5 Scope and Organization of the Book 10
References 14
2 Rough-Fuzzy Hybridization and Granular Computing 21
2.1 Introduction 21
2.2 Fuzzy Sets 22
2.3 Rough Sets 23
2.4 Emergence of Rough-Fuzzy Computing 26
2.4.1 Granular Computing 26
2.4.2 Computational Theory of Perception and f -Granulation 26
2.4.3 Rough-Fuzzy Computing 28
2.5 Generalized Rough Sets 29
2.6 Entropy Measures 30
2.7 Conclusion and Discussion 36
References 37
3 Rough-Fuzzy Clustering: Generalized c-Means Algorithm 47
3.1 Introduction 47
3.2 Existing c-Means Algorithms 49
3.2.1 Hard c-Means 49
3.2.2 Fuzzy c-Means 50
3.2.3 Possibilistic c-Means 51
3.2.4 Rough c-Means 52
3.3 Rough-Fuzzy-Possibilistic c-Means 53
3.3.1 Objective Function 54
3.3.2 Cluster Prototypes 55
3.3.3 Fundamental Properties 56
3.3.4 Convergence Condition 57
3.3.5 Details of the Algorithm 59
3.3.6 Selection of Parameters 60
3.4 Generalization of Existing c-Means Algorithms 61
3.4.1 RFCM: Rough-Fuzzy c-Means 61
3.4.2 RPCM: Rough-Possibilistic c-Means 62
3.4.3 RCM: Rough c-Means 63
3.4.4 FPCM: Fuzzy-Possibilistic c-Means 64
3.4.5 FCM: Fuzzy c-Means 64
3.4.6 PCM: Possibilistic c-Means 64
3.4.7 HCM: Hard c-Means 65
3.5 Quantitative Indices for Rough-F
