Recent advances in hybrid metaheuristics for dataclustering /

"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"--

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: De, Sourav, 1979- (Editor ), Dey, Sandip, 1977- (Editor ), Bhattacharyya, Siddhartha, 1975- (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, NJ : John Wiley & Sons, Inc., 2020.
Colección:The Wiley Series in Intelligent Signal and Data Processing
Temas:
Cluster analysis > Data processing.
Metaheuristics.
Classification automatique (Statistique) > Informatique.
Métaheuristiques.
Cluster analysis > Data processing
Metaheuristics
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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

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