Includes bibliographical references and index.

"The book will elaborate on the fundamentals of different meta-heuristics and their application to data clustering. As a result, it will pave the way for designing and developing hybrid meta-heuristics to be applied to data clustering"-- Provided by publisher

Online resource; title from digital title page (viewed on July 27, 2020).

Cover -- Title Page -- Copyright -- Contents -- List of Contributors -- Series Preface -- Preface -- Chapter 1 Metaheuristic Algorithms in Fuzzy Clustering -- 1.1 Introduction -- 1.2 Fuzzy Clustering -- 1.2.1 Fuzzy c-means (FCM) clustering -- 1.3 Algorithm -- 1.3.1 Selection of Cluster Centers -- 1.4 Genetic Algorithm -- 1.5 Particle Swarm Optimization -- 1.6 Ant Colony Optimization -- 1.7 Artificial Bee Colony Algorithm -- 1.8 Local Search-Based Metaheuristic Clustering Algorithms -- 1.9 Population-Based Metaheuristic Clustering Algorithms -- 1.9.1 GA-Based Fuzzy Clustering

1.9.2 PSO-Based Fuzzy Clustering -- 1.9.3 Ant Colony Optimization-Based Fuzzy Clustering -- 1.9.4 Artificial Bee Colony Optimization-Based Fuzzy Clustering -- 1.9.5 Differential Evolution-Based Fuzzy Clustering -- 1.9.6 Firefly Algorithm-Based Fuzzy Clustering -- 1.10 Conclusion -- References -- Chapter 2 Hybrid Harmony Search Algorithm to Solve the Feature Selection for Data Mining Applications -- 2.1 Introduction -- 2.2 Research Framework -- 2.3 Text Preprocessing -- 2.3.1 Tokenization -- 2.3.2 Stop Words Removal -- 2.3.3 Stemming -- 2.3.4 Text Document Representation

2.3.5 Term Weight (TF-IDF) -- 2.4 Text Feature Selection -- 2.4.1 Mathematical Model of the Feature Selection Problem -- 2.4.2 Solution Representation -- 2.4.3 Fitness Function -- 2.5 Harmony Search Algorithm -- 2.5.1 Parameters Initialization -- 2.5.2 Harmony Memory Initialization -- 2.5.3 Generating a New Solution -- 2.5.4 Update Harmony Memory -- 2.5.5 Check the Stopping Criterion -- 2.6 Text Clustering -- 2.6.1 Mathematical Model of the Text Clustering -- 2.6.2 Find Clusters Centroid -- 2.6.3 Similarity Measure -- 2.7 k-means text clustering algorithm -- 2.8 Experimental Results

2.8.1 Evaluation Measures -- 2.8.1.1 F-measure Based on Clustering Evaluation -- 2.8.1.2 Accuracy Based on Clustering Evaluation -- 2.8.2 Results and Discussions -- 2.9 Conclusion -- References -- Chapter 3 Adaptive Position-Based Crossover in the Genetic Algorithm for Data Clustering -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 Clustering -- 3.2.1.1 k-means Clustering -- 3.2.2 Genetic Algorithm -- 3.3 Related Works -- 3.3.1 GA-Based Data Clustering by Binary Encoding -- 3.3.2 GA-Based Data Clustering by Real Encoding -- 3.3.3 GA-Based Data Clustering for Imbalanced Datasets

3.4 Proposed Model -- 3.5 Experimentation -- 3.5.1 Experimental Settings -- 3.5.2 DB Index -- 3.5.3 Experimental Results -- 3.6 Conclusion -- References -- Chapter 4 Application of Machine Learning in the Social Network -- 4.1 Introduction -- 4.1.1 Social Media -- 4.1.2 Big Data -- 4.1.3 Machine Learning -- 4.1.4 Natural Language Processing (NLP) -- 4.1.5 Social Network Analysis -- 4.2 Application of Classification Models in Social Networks -- 4.2.1 Spam Content Detection -- 4.2.2 Topic Modeling and Labeling -- 4.2.3 Human Behavior Analysis -- 4.2.4 Sentiment Analysis

John Wiley and Sons Wiley Online Library: Complete oBooks