An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques
Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics and their application to data clustering. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. The authorsnoted experts on the topicprovide a text that can aid in the design and development of hybrid metaheuristics to be applied to data clustering.
The book includes performance analysis of the hybrid metaheuristics in relationship to their conventional counterparts. In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms. The text offers a step-by-step guide in the build-up of hybrid metaheuristics and to enhance comprehension. In addition, the book contains a range of real-life case studies and their applications. This important text:
- Includes performance analysis of the hybrid metaheuristics as related to their conventional counterparts
- Offers an in-depth analysis of a range of optimization algorithms
- Highlights a review of data clustering
- Contains a detailed overview of different standard metaheuristics in current use
- Presents a step-by-step guide to the build-up of hybrid metaheuristics
- Offers real-life case studies and applications
Written for researchers, students and academics in computer science, mathematics, and engineering, Recent Advances in Hybrid Metaheuristics for Data Clustering provides a text that explores the current data clustering approaches using a range of computational intelligence techniques.
Table of Contents
List of Contributors xiii
Series Preface xv
Preface xvii
1 Metaheuristic Algorithms in Fuzzy Clustering 1
Sourav De, Sandip Dey, and Siddhartha Bhattacharyya
1.1 Introduction 1
1.2 Fuzzy Clustering 1
1.2.1 Fuzzy c-means (FCM) clustering 2
1.3 Algorithm 2
1.3.1 Selection of Cluster Centers 3
1.4 Genetic Algorithm 3
1.5 Particle Swarm Optimization 5
1.6 Ant Colony Optimization 6
1.7 Artificial Bee Colony Algorithm 7
1.8 Local Search-Based Metaheuristic Clustering Algorithms 7
1.9 Population-Based Metaheuristic Clustering Algorithms 8
1.9.1 GA-Based Fuzzy Clustering 8
1.9.2 PSO-Based Fuzzy Clustering 9
1.9.3 Ant Colony Optimization-Based Fuzzy Clustering 10
1.9.4 Artificial Bee Colony Optimization-Based Fuzzy Clustering 10
1.9.5 Differential Evolution-Based Fuzzy Clustering 11
1.9.6 Firefly Algorithm-Based Fuzzy Clustering 12
1.10 Conclusion 13
References 13
2 Hybrid Harmony Search Algorithm to Solve the Feature Selection for Data Mining Applications 19
Laith Mohammad Abualigah, Mofleh Al-diabat, Mohammad Al Shinwan, Khaldoon Dhou, Bisan Alsalibi, Essam Said Hanandeh, and Mohammad Shehab
2.1 Introduction 19
2.2 Research Framework 21
2.3 Text Preprocessing 22
2.3.1 Tokenization 22
2.3.2 StopWords Removal 22
2.3.3 Stemming 23
2.3.4 Text Document Representation 23
2.3.5 TermWeight (TF-IDF) 23
2.4 Text Feature Selection 24
2.4.1 Mathematical Model of the Feature Selection Problem 24
2.4.2 Solution Representation 24
2.4.3 Fitness Function 24
2.5 Harmony Search Algorithm 25
2.5.1 Parameters Initialization 25
2.5.2 Harmony Memory Initialization 26
2.5.3 Generating a New Solution 26
2.5.4 Update Harmony Memory 27
2.5.5 Check the Stopping Criterion 27
2.6 Text Clustering 27
2.6.1 Mathematical Model of the Text Clustering 27
2.6.2 Find Clusters Centroid 27
2.6.3 Similarity Measure 28
2.7 k-means text clustering algorithm 28
2.8 Experimental Results 29
2.8.1 Evaluation Measures 29
2.8.1.1 F-measure Based on Clustering Evaluation 30
2.8.1.2 Accuracy Based on Clustering Evaluation 31
2.8.2 Results and Discussions 31
2.9 Conclusion 34
References 34
3 Adaptive Position-Based Crossover in the Genetic Algorithm for Data Clustering 39
Arnab Gain and Prasenjit Dey
3.1 Introduction 39
3.2 Preliminaries 40
3.2.1 Clustering 40
3.2.1.1 k-means Clustering 40
3.2.2 Genetic Algorithm 41
3.3 RelatedWorks 42
3.3.1 GA-Based Data Clustering by Binary Encoding 42
3.3.2 GA-Based Data Clustering by Real Encoding 43
3.3.3 GA-Based Data Clustering for Imbalanced Datasets 44
3.4 Proposed Model 44
3.5 Experimentation 46
3.5.1 Experimental Settings 46
3.5.2 DB Index 47
3.5.3 Experimental Results 49
3.6 Conclusion 51
References 57
4 Application of Machine Learning in the Social Network 61
Belfin R. V., E. Grace Mary Kanaga, and Suman Kundu
4.1 Introduction 61
4.1.1 Social Media 61
4.1.2 Big Data 62
4.1.3 Machine Learning 62
4.1.4 Natural Language Processing (NLP) 63
4.1.5 Social Network Analysis 64
4.2 Application of Classification Models in Social Networks 64
4.2.1 Spam Content Detection 65
4.2.2 Topic Modeling and Labeling 65
4.2.3 Human Behavior Analysis 67
4.2.4 Sentiment Analysis 68
4.3 Application of Clustering Models in Social Networks 68
4.3.1 Recommender Systems 69
4.3.2 Sentiment Analysis 70
4.3.3 Information Spreading or Promotion 70
4.3.4 Geolocation-Specific Applications 70
4.4 Application of Regression Models in Social Networks 71
4.4.1 Social Network and Human Behavior 71
4.4.2 Emotion Contagion through Social Networks 73
4.4.3 Recommender Systems in Social Networks 74
4.5 Application of Evolutionary Computing and Deep Learning in Social Networks 74
4.5.1 Evolutionary Computing and Social Network 75
4.5.2 Deep Learning and Social Networks 75
4.6 Summary 76
Acknowledgments 77
References 78
5 Predicting Students’ Grades Using CART, ID3, and Multiclass SVM Optimized by the Genetic Algorithm (GA): A Case Study 85
Debanjan Konar, Ruchita Pradhan, Tania Dey, Tejaswini Sapkota, and Prativa Rai
5.1 Introduction 85
5.2 Literature Review 87
5.3 Decision Tree Algorithms: ID3 and CART 88
5.4 Multiclass Support Vector Machines (SVMs) Optimized by the Genetic Algorithm (GA) 90
5.4.1 Genetic Algorithms for SVM Model Selection 92
5.5 Preparation of Datasets 93
5.6 Experimental Results and Discussions 95
5.7 Conclusion 96
References 96
6 Cluster Analysis of Health Care Data Using Hybrid Nature-Inspired Algorithms 101
Kauser Ahmed P, Rishabh Agrawal
6.1 Introduction 101
6.2 RelatedWork 102
6.2.1 Firefly Algorithm 102
6.2.2 k-means Algorithm 103
6.3 Proposed Methodology 104
6.4 Results and Discussion 106
6.5 Conclusion 110
References 111
7 Performance Analysis Through a Metaheuristic Knowledge Engine 113
Indu Chhabra and Gunmala Suri
7.1 Introduction 113
7.2 Data Mining and Metaheuristics 114
7.3 Problem Description 115
7.4 Association Rule Learning 116
7.4.1 Association Mining Issues 116
7.4.2 Research Initiatives and Projects 116
7.5 Literature Review 117
7.6 Methodology 119
7.6.1 Phase 1: Pattern Search 120
7.6.2 Phase 2: Rule Mining 120
7.6.3 Phase 3: Knowledge Derivation 121
7.7 Implementation 121
7.7.1 Test Issues 121
7.7.2 System Evaluation 121
7.7.2.1 Indicator Matrix Formulation 122
7.7.2.2 Phase 1: Frequent Pattern Derivation 123
7.7.2.3 Phase 2: Association Rule Framing 123
7.7.2.4 Phase 3: Knowledge Discovery Through Metaheuristic Implementation 123
7.8 Performance Analysis 124
7.9 Research Contributions and Future Work 125
7.10 Conclusion 126
References 126
8 Magnetic Resonance Image Segmentation Using a Quantum-Inspired Modified Genetic Algorithm (QIANA) Based on FRCM 129
Sunanda Das, Sourav De, Sandip Dey, and Siddhartha Bhattacharyya
8.1 Introduction 129
8.2 Literature Survey 131
8.3 Quantum Computing 133
8.3.1 Quoit-Quantum Bit 133
8.3.2 Entanglement 133
8.3.3 Measurement 133
8.3.4 Quantum Gate 134
8.4 Some Quality Evaluation Indices for Image Segmentation 134
8.4.1 F(I) 134
8.4.2 F’(I) 135
8.4.3 Q(I) 135
8.5 Quantum-Inspired Modified Genetic Algorithm (QIANA)-Based FRCM 135
8.5.1 Quantum-Inspired MEGA (QIANA)-Based FRCM 136
8.6 Experimental Results and Discussion 139
8.7 Conclusion 147
References 147
9 A Hybrid Approach Using the k-means and Genetic Algorithms for Image Color Quantization 151
Marcos Roberto e Souza, Anderson Carlos Sousa e Santos, and Helio Pedrini
9.1 Introduction 151
9.2 Background 152
9.3 Color Quantization Methodology 154
9.3.1 Crossover Operators 157
9.3.2 Mutation Operators 158
9.3.3 Fitness Function 158
9.4 Results and Discussions 159
9.5 Conclusions and Future Work 168
Acknowledgments 168
References 168
Index 173
