Evolutionary Data Clustering: Algorithms and Applications (Algorithms for Intelligent Systems)

✍ Scribed by Ibrahim Aljarah (editor), Hossam Faris (editor), Seyedali Mirjalili (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 253
• Category: Library

✦ Synopsis

This book provides an in-depth analysis of the current evolutionary clustering techniques. It discusses the most highly regarded methods for data clustering. The book provides literature reviews about single objective and multi-objective evolutionary clustering algorithms. In addition, the book provides a comprehensive review of the fitness functions and evaluation measures that are used in most of evolutionary clustering algorithms. Furthermore, it provides a conceptual analysis including definition, validation and quality measures, applications, and implementations for data clustering using classical and modern nature-inspired techniques. It features a range of proven and recent nature-inspired algorithms used to data clustering, including particle swarm optimization, ant colony optimization, grey wolf optimizer, salp swarm algorithm, multi-verse optimizer, Harris hawks optimization, beta-hill climbing optimization. The book also covers applications of evolutionary data clustering indiverse fields such as image segmentation, medical applications, and pavement infrastructure asset management.

✦ Table of Contents

Preface
Contents
Editors and Contributors
Introduction to Evolutionary Data Clustering and Its Applications
1 Introduction
2 Clustering
3 Approaches of Clustering
3.1 Partitioning
3.2 Hierarchical
4 Evolutionary Clustering
4.1 GA Algorithm for Clustering
5 Applications of Evolutionary Clustering
5.1 Data Mining and Machine Learning
5.2 Image Processing and Pattern Recognition
5.3 Web Search and Information Retrieval
5.4 Bioinformatics
5.5 Business Intelligence and Security
6 Conclusion
References
A Comprehensive Review of Evaluation and Fitness Measures for Evolutionary Data Clustering
1 Introduction
2 Evolutionary Clustering Validation Indices (CVI)
2.1 External Measures
2.2 Internal Measures
2.3 Other Non-evolutionary CVIs
3 Fitness Evaluation Indices
3.1 Root Mean Squared Error
3.2 (Intra and Inter) Cluster Distances
3.3 Jaccard Distance
3.4 Compactness and Separation Measures of Clusters
3.5 Clusters Density
4 Conclusion
References
A Grey Wolf-Based Clustering Algorithm for Medical Diagnosis Problems
1 Introduction
2 Related Work
3 Grey Wolf Optimizer for Clustering
3.1 GWO Algorithm
3.2 Clustering with GWO
4 Experimental Results
4.1 Data Sets
4.2 Algorithms and Parameters Selection
4.3 Fitness Function
4.4 Results and Discussion
5 Conclusion
References
EEG-Based Person Identification Using Multi-Verse Optimizer as Unsupervised Clustering Techniques
1 Introduction
2 Preliminaries
2.1 k-Means
2.2 Principles for Multi-Verse Optimizer (MVO)
3 Hybridizing EEG with MVO for Unsupervised Person Identification: Proposed Method
3.1 Signal Acquisition
3.2 Signal Pre-processing
3.3 EEG Feature Extraction
3.4 EEG Signal Clustering
4 Results and Discussions
4.1 Evaluation Measures
5 Conclusions and Future Work
References
Capacitated Vehicle Routing Problem—A New Clustering Approach Based on Hybridization of Adaptive Particle Swarm Optimization and Grey Wolf Optimization
1 Introduction and Related Works
2 Mathematical Models of CVRP
3 Our Algorithm
3.1 Grey Wolf Optimization
3.2 Particle Swarm Optimization
3.3 Adaptive Particle Swarm Optimization (APSO)
3.4 Adaptive Particle Swarm Optimization–Grey Wolf Optimization (APSOGWO)
3.5 Clustering with Evolutionary Algorithms
4 Computational Results
5 Conclusion
References
A Hybrid Salp Swarm Algorithm with β-Hill Climbing Algorithm for Text Documents Clustering
1 Introduction
2 Related Works
3 Text Document Clustering Problem
3.1 Problem Formulation
3.2 Preprocessing of Text Document
3.3 Clustering Algorithm and Similarity Measures
4 Salp Swarm Algorithm (SSA)
5 β-Hill Climbing Algorithm
6 The Proposed Hybrid Salp Swarm Algorithm for TDC
6.1 Improving the Initial Candidate Solutions Quality Using β-Hill Climbing
6.2 Improving the Best Solution of Salp Swarm Algorithm with β-Hill Climbing
7 Results and Discussion
7.1 The Experimental Design
7.2 Experimental Benchmark Datasets and Parameter Setting
7.3 Evaluation Measures
7.4 Analysis of Results
7.5 Convergence Analysis
8 Conclusion and Future Works
References
Controlling Population Diversity of Harris Hawks Optimization Algorithm Using Self-adaptive Clustering Approach
1 Introduction
2 Proposed Approach
3 Results and Analysis
4 Conclusion and Future Works
References
A Review of Multiobjective Evolutionary Algorithms for Data Clustering Problems
1 Introduction
2 Clustering Problem
3 Formulating Clustering as an Optimization Problem
4 Multi-objective Optimization Problem
5 Importance of Multi-objective Clustering
6 Framework of Evolutionary Multiple Objectives Clustering
7 Overview of Multi-objective Clustering Techniques
7.1 Encoding Schemes of Evolutionary Clustering Solution
7.2 Objective Functions of MOEA for Clustering Problem
7.3 Design of Evolutionary Operators
7.4 Maintaining Non-dominated Solutions
7.5 Deciding the Final Clustering Solution
8 Applications of Multi-objective Evolutionary Clustering
8.1 Image Applications
8.2 Bioinformatics
8.3 Social Networks
8.4 Other Applications
9 Conclusion
References
A Review of Evolutionary Data Clustering Algorithms for Image Segmentation
1 Introduction
2 Preliminaries
2.1 Clustering
2.2 Metaheuristic Algorithms
2.3 Image Segmentation
3 Applications of Clustering with Metaheuristic Algorithms on Image Segmentation
3.1 Medical Image Segmentation
3.2 Multiobjective Image Segmentation
3.3 Multilevel Thresholding Image Segmentation
3.4 Other Applications on Image Segmentation
4 Conclusion and Future Directions
References
Pavement Infrastructure Asset Management Using Clustering-Based Ant Colony Optimization
1 Introduction
2 Theoretical Background
2.1 Optimization Techniques
2.2 Ant Colony Optimization (ACO)
2.3 ACO Behavioral Process
2.4 Ant Searching Transition Rule
2.5 Local Retracing and Updating Rule
2.6 Evaporation of Pheromone Deposits
2.7 Global Implementation Algorithm
3 ACO in Pavement Management System
3.1 Prospects
3.2 Application in Pavement Management Systems
4 Ant Colony Optimization-Based Clustering Analysis in PMS
5 Conclusion
References
A Classification Approach Based on Evolutionary Clustering and Its Application for Ransomware Detection
1 Introduction
2 Related Work
3 Grey Wolf Optimizer (GWO)
4 Proposed Approach
4.1 Clustering with GWO
4.2 Classification with SVM
5 Dataset Description
6 Experimental Results
7 Conclusion
References