Recent Advances in Hybrid Metaheuristics for Data Clustering

Sourav De, PhD, is an Associate Professor of Computer Science and Engineering at Cooch Behar Government Engineering College, West Bengal, India. Sandip Dey, PhD, is an Assistant Professor of Computer Science at Sukanta Mahavidyalaya, Dhupguri, Jalpaiguri, India. Siddhartha Bhattacharyya, PhD, is a Professor of Computer Science and Engineering at CHRIST (Deemed to be University), Bangalore, India.

• List of Contributors xiiiSeries Preface xvPreface xvii1 Metaheuristic Algorithms in Fuzzy Clustering 1Sourav De, Sandip Dey, and Siddhartha Bhattacharyya1.1 Introduction 11.2 Fuzzy Clustering 11.2.1 Fuzzy c-means (FCM) clustering 21.3 Algorithm 21.3.1 Selection of Cluster Centers 31.4 Genetic Algorithm 31.5 Particle Swarm Optimization 51.6 Ant Colony Optimization 61.7 Artificial Bee Colony Algorithm 71.8 Local Search-Based Metaheuristic Clustering Algorithms 71.9 Population-Based Metaheuristic Clustering Algorithms 81.9.1 GA-Based Fuzzy Clustering 81.9.2 PSO-Based Fuzzy Clustering 91.9.3 Ant Colony Optimization–Based Fuzzy Clustering 101.9.4 Artificial Bee Colony Optimization–Based Fuzzy Clustering 101.9.5 Differential Evolution–Based Fuzzy Clustering 111.9.6 Firefly Algorithm–Based Fuzzy Clustering 121.10 Conclusion 13References 132 Hybrid Harmony Search Algorithm to Solve the Feature Selection for Data Mining Applications 19Laith Mohammad Abualigah, Mofleh Al-diabat, Mohammad Al Shinwan, Khaldoon Dhou, Bisan Alsalibi, Essam Said Hanandeh, and Mohammad Shehab2.1 Introduction 192.2 Research Framework 212.3 Text Preprocessing 222.3.1 Tokenization 222.3.2 StopWords Removal 222.3.3 Stemming 232.3.4 Text Document Representation 232.3.5 TermWeight (TF-IDF) 232.4 Text Feature Selection 242.4.1 Mathematical Model of the Feature Selection Problem 242.4.2 Solution Representation 242.4.3 Fitness Function 242.5 Harmony Search Algorithm 252.5.1 Parameters Initialization 252.5.2 Harmony Memory Initialization 262.5.3 Generating a New Solution 262.5.4 Update Harmony Memory 272.5.5 Check the Stopping Criterion 272.6 Text Clustering 272.6.1 Mathematical Model of the Text Clustering 272.6.2 Find Clusters Centroid 272.6.3 Similarity Measure 282.7 k-means text clustering algorithm 282.8 Experimental Results 292.8.1 Evaluation Measures 292.8.1.1 F-measure Based on Clustering Evaluation 302.8.1.2 Accuracy Based on Clustering Evaluation 312.8.2 Results and Discussions 312.9 Conclusion 34References 343 Adaptive Position–Based Crossover in the Genetic Algorithm for Data Clustering 39Arnab Gain and Prasenjit Dey3.1 Introduction 393.2 Preliminaries 403.2.1 Clustering 403.2.1.1 k-means Clustering 403.2.2 Genetic Algorithm 413.3 RelatedWorks 423.3.1 GA-Based Data Clustering by Binary Encoding 423.3.2 GA-Based Data Clustering by Real Encoding 433.3.3 GA-Based Data Clustering for Imbalanced Datasets 443.4 Proposed Model 443.5 Experimentation 463.5.1 Experimental Settings 463.5.2 DB Index 473.5.3 Experimental Results 493.6 Conclusion 51References 574 Application of Machine Learning in the Social Network 61Belfin R. V., E. Grace Mary Kanaga, and Suman Kundu4.1 Introduction 614.1.1 Social Media 614.1.2 Big Data 624.1.3 Machine Learning 624.1.4 Natural Language Processing (NLP) 634.1.5 Social Network Analysis 644.2 Application of Classification Models in Social Networks 644.2.1 Spam Content Detection 654.2.2 Topic Modeling and Labeling 654.2.3 Human Behavior Analysis 674.2.4 Sentiment Analysis 684.3 Application of Clustering Models in Social Networks 684.3.1 Recommender Systems 694.3.2 Sentiment Analysis 704.3.3 Information Spreading or Promotion 704.3.4 Geolocation-Specific Applications 704.4 Application of Regression Models in Social Networks 714.4.1 Social Network and Human Behavior 714.4.2 Emotion Contagion through Social Networks 734.4.3 Recommender Systems in Social Networks 744.5 Application of Evolutionary Computing and Deep Learning in Social Networks 744.5.1 Evolutionary Computing and Social Network 754.5.2 Deep Learning and Social Networks 754.6 Summary 76Acknowledgments 77References 785 Predicting Students’ Grades Using CART, ID3, and Multiclass SVM Optimized by the Genetic Algorithm (GA): A Case Study 85Debanjan Konar, Ruchita Pradhan, Tania Dey, Tejaswini Sapkota, and Prativa Rai5.1 Introduction 855.2 Literature Review 875.3 Decision Tree Algorithms: ID3 and CART 885.4 Multiclass Support Vector Machines (SVMs) Optimized by the Genetic Algorithm (GA) 905.4.1 Genetic Algorithms for SVM Model Selection 925.5 Preparation of Datasets 935.6 Experimental Results and Discussions 955.7 Conclusion 96References 966 Cluster Analysis of Health Care Data Using Hybrid Nature-Inspired Algorithms 101Kauser Ahmed P, Rishabh Agrawal6.1 Introduction 1016.2 RelatedWork 1026.2.1 Firefly Algorithm 1026.2.2 k-means Algorithm 1036.3 Proposed Methodology 1046.4 Results and Discussion 1066.5 Conclusion 110References 1117 Performance Analysis Through a Metaheuristic Knowledge Engine 113Indu Chhabra and Gunmala Suri7.1 Introduction 1137.2 Data Mining and Metaheuristics 1147.3 Problem Description 1157.4 Association Rule Learning 1167.4.1 Association Mining Issues 1167.4.2 Research Initiatives and Projects 1167.5 Literature Review 1177.6 Methodology 1197.6.1 Phase 1: Pattern Search 1207.6.2 Phase 2: Rule Mining 1207.6.3 Phase 3: Knowledge Derivation 1217.7 Implementation 1217.7.1 Test Issues 1217.7.2 System Evaluation 1217.7.2.1 Indicator Matrix Formulation 1227.7.2.2 Phase 1: Frequent Pattern Derivation 1237.7.2.3 Phase 2: Association Rule Framing 1237.7.2.4 Phase 3: Knowledge Discovery Through Metaheuristic Implementation 1237.8 Performance Analysis 1247.9 Research Contributions and Future Work 1257.10 Conclusion 126References 1268 Magnetic Resonance Image Segmentation Using a Quantum-Inspired Modified Genetic Algorithm (QIANA) Based on FRCM 129Sunanda Das, Sourav De, Sandip Dey, and Siddhartha Bhattacharyya8.1 Introduction 1298.2 Literature Survey 1318.3 Quantum Computing 1338.3.1 Quoit-Quantum Bit 1338.3.2 Entanglement 1338.3.3 Measurement 1338.3.4 Quantum Gate 1348.4 Some Quality Evaluation Indices for Image Segmentation 1348.4.1 F(I) 1348.4.2 F’(I) 1358.4.3 Q(I) 1358.5 Quantum-Inspired Modified Genetic Algorithm (QIANA)–Based FRCM 1358.5.1 Quantum-Inspired MEGA (QIANA)–Based FRCM 1368.6 Experimental Results and Discussion 1398.7 Conclusion 147References 1479 A Hybrid Approach Using the k-means and Genetic Algorithms for Image Color Quantization 151Marcos Roberto e Souza, Anderson Carlos Sousa e Santos, and Helio Pedrini9.1 Introduction 1519.2 Background 1529.3 Color Quantization Methodology 1549.3.1 Crossover Operators 1579.3.2 Mutation Operators 1589.3.3 Fitness Function 1589.4 Results and Discussions 1599.5 Conclusions and Future Work 168Acknowledgments 168References 168Index 173