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E-raamat: Recent Advances in Hybrid Metaheuristics for Data Clustering [Wiley Online]

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Recent Advances in Hybrid Metaheuristics for Data ClusteringSuurem pilt
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Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119551621
"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"--

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 authors—noted experts on the topic—provide 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.

List of Contributors
xiii
Series Preface xv
Preface xvii
1 Metaheuristic Algorithms In Fuzzy Clustering
1(18)
Sourav De
Sandip Dey
Siddhartha Bhattacharyya
1.1 Introduction
1(1)
1.2 Fuzzy Clustering
1(1)
1.2.1 Fuzzy c-means (FCM) clustering
2(1)
1.3 Algorithm
2(1)
1.3.1 Selection of Cluster Centers
3(1)
1.4 Genetic Algorithm
3(2)
1.5 Particle Swarm Optimization
5(1)
1.6 Ant Colony Optimization
6(1)
1.7 Artificial Bee Colony Algorithm
7(1)
1.8 Local Search-Based Metaheuristic Clustering Algorithms
7(1)
1.9 Population-Based Metaheuristic Clustering Algorithms
8(2)
1.9.1 GA-Based Fuzzy Clustering
8(1)
1.9.2 PSO-Based Fuzzy Clustering
9(1)
1.9.3 Ant Colony Optimization-Based Fuzzy Clustering
10(1)
1.9 4 Artificial Bee Colony Optimization-Based Fuzzy Clustering
10(3)
1.9.5 Differential Evolution-Based Fuzzy Clustering
11(1)
1.9.6 Firefly Algorithm-Based Fuzzy Clustering
12(1)
1.10 Conclusion
13(6)
References
13(6)
2 Hybrid Harmony Search Algorithm To Solve The Feature Selection For Data Mining Applications
19(20)
Laith Mohammad Abualigah
Mofleh Al-diabat
Mohammad Al Shinwan
Khaldoon Dhou
Bisan Atsalibi
Essam Said Hanandeh
Mohammad Shehab
2.1 Introduction
19(2)
2.2 Research Framework
21(1)
2.3 Text Preprocessing
22(2)
2.3.1 Tokenization
22(1)
2.3.2 Stop Words Removal
22(1)
2.3.3 Stemming
23(1)
2.3.4 Text Document Representation
23(1)
2.3.5 Term Weight (TF-IDF)
23(1)
2.4 Text Feature Selection
24(1)
2.4.1 Mathematical Model of the Feature Selection Problem
24(1)
2.4.2 Solution Representation
24(1)
2.4.3 Fitness Function
24(1)
2.5 Harmony Search Algorithm
25(2)
2.5.1 Parameters Initialization
25(1)
2.5.2 Harmony Memory Initialization
26(1)
2.5.3 Generating a New Solution
26(1)
2.5.4 Update Harmony Memory
27(1)
2.5.5 Check the Stopping Criterion
27(1)
2.6 Text Clustering
27(1)
2.6.1 Mathematical Model of the Text Clustering
27(1)
2.6.2 Find Clusters Centroid
27(1)
2.6.3 Similarity Measure
28(1)
2.7 k-means text clustering algorithm
28(1)
2.8 Experimental Results
29(5)
2.8.1 Evaluation Measures
29(1)
2.8.1.1 F-measure Based on Clustering Evaluation
30(1)
2.8.1.2 Accuracy Based on Clustering Evaluation
31(1)
2.8.2 Results and Discussions
31(3)
2.9 Conclusion
34(5)
References
34(5)
3 Adaptive Position -- Based Crossover In The Genetic Algorithm For Data Clustering
39(22)
Arnab Gain
Prasenjit Dey
3.1 Introduction
39(1)
3.2 Preliminaries
40(2)
3.2.1 Clustering
40(1)
3.2.1.1 k-means Clustering
40(1)
3.2.2 Genetic Algorithm
41(1)
3.3 Related Works
42(2)
3.3.1 GA-Based Data Clustering by Binary Encoding
42(1)
3.3.2 GA-Based Data Clustering by Real Encoding
43(1)
3.3.3 GA-Based Data Clustering for Imbalanced Datasets
44(1)
3.4 Proposed Model
44(2)
3.5 Experimentation
46(5)
3.5.1 Experimental Settings
46(1)
3.5.2 DB Index
47(2)
3.5.3 Experimental Results
49(2)
3.6 Conclusion
51(10)
References
57(4)
4 Application Of Machine Learning In The Social Network
61(24)
Belfin R. V. E. Grace Mary Kanaga
Suman Kundu
4.1 Introduction
61(3)
4.1.1 Social Media
61(1)
4.1.2 Big Data
62(1)
4.1.3 Machine Learning
62(1)
4.1.4 Natural Language Processing (NLP)
63(1)
4.1.5 Social Network Analysis
64(1)
4.2 Application of Classification Models in Social Networks
64(4)
4.2.1 Spam Content Detection
65(1)
4.2.2 Topic Modeling and Labeling
65(2)
4.2.3 Human Behavior Analysis
67(1)
4.2.4 Sentiment Analysis
68(1)
4.3 Application of Clustering Models in Social Networks
68(3)
4.3.1 Recommender Systems
69(1)
4.3.2 Sentiment Analysis
70(1)
4.3.3 Information Spreading or Promotion
70(1)
4.3.4 Geolocation-Specific Applications
70(1)
4.4 Application of Regression Models in Social Networks
71(3)
4.4.1 Social Network and Human Behavior
71(2)
4.4.2 Emotion Contagion through Social Networks
73(1)
4.4.3 Recommender Systems in Social Networks
74(1)
4.5 Application of Evolutionary Computing and Deep Learning in Social Networks
74(2)
4.5.1 Evolutionary Computing and Social Network
75(1)
4.5.2 Deep Learning and Social Networks
75(1)
4.6 Summary
76(9)
Acknowledgments
77(1)
References
78(7)
5 Predicting Students' Grades Using Cart, Id3, And Multiclass Svm Optimized By The Genetic Algorithm (Ga): A Case Study
85(16)
Debanjan Konar
Ruchita Pradhan
Tania Dey
Tejaswini Sapkota
Prativa Rai
5.1 Introduction
85(2)
5.2 Literature Review
87(1)
5.3 Decision Tree Algorithms: ID3 and CART
88(2)
5.4 Multiclass Support Vector Machines (SVMs) Optimized by the Genetic Algorithm (GA)
90(3)
5.4.1 Genetic Algorithms for SVM Model Selection
92(1)
5.5 Preparation of Datasets
93(2)
5.6 Experimental Results and Discussions
95(1)
5.7 Conclusion
96(5)
References
96(5)
6 Cluster Analysis Of Health Care Data Using Hybrid Nature-Inspired Algorithms
101(12)
Kauser Ahmed P.
Rishabh Agrawal
6.1 Introduction
101(1)
6.2 Related Work
102(2)
6.2.1 Firefly Algorithm
102(1)
6.2.2 k-means Algorithm
103(1)
6.3 Proposed Methodology
104(2)
6.4 Results and Discussion
106(4)
6.5 Conclusion
110(3)
References
111(2)
7 Performance Analysis Through A Metaheuristic Knowledge Engine
113(16)
Indu Chhabra
Gunmala Suri
7.1 Introduction
113(1)
7.2 Data Mining and Metaheuristics
114(1)
7.3 Problem Description
115(1)
7.4 Association Rule Learning
116(1)
7.4.1 Association Mining Issues
116(1)
7.4.2 Research Initiatives and Projects
116(1)
7.5 Literature Review
117(2)
7.6 Methodology
119(2)
7.6.1 Phase 1: Pattern Search
120(1)
7.6.2 Phase 2: Rule Mining
120(1)
7.6.3 Phase 3: Knowledge Derivation
121(1)
7.1 Implementation
121(3)
7.7.1 Test Issues
121(1)
7.7.2 System Evaluation
121(1)
7.7.2.1 Indicator Matrix Formulation
122(1)
7.7.2.2 Phase 1: Frequent Pattern Derivation
123(1)
7.7.2.3 Phase 2: Association Rule Framing
123(1)
7.7.2.4 Phase 3: Knowledge Discovery Through Metaheuristic Implementation
123(1)
7.8 Performance Analysis
124(1)
7.9 Research Contributions and Future Work
125(1)
7.10 Conclusion
126(3)
References
126(3)
8 Magnetic Resonance Image Segmentation Using A Quantum-Inspired Modified Genetic Algorithm (Qiana) Based On Frcm
129(22)
Sunanda Das
Sourav De
Sandip Dey
Siddhartha Bhattacharyya
8.1 Introduction
129(2)
8.2 Literature Survey
131(2)
8.3 Quantum Computing
133(1)
8.3.1 Quoit-Quantum Bit
133(1)
8.3.2 Entanglement
133(1)
8.3.3 Measurement
133(1)
8.3.4 Quantum Gate
134(1)
8.4 Some Quality Evaluation Indices for Image Segmentation
134(1)
8.4.1 F(I)
134(1)
8.4.2 F'(I)
135(1)
8.4.3 Q(I)
135(1)
8.5 Quantum-Inspired Modified Genetic Algorithm (QIANA)-Based FRCM
135(4)
8.5.1 Quantum-Inspired MEGA (QIANA)-Based FRCM
136(3)
8.6 Experimental Results and Discussion
139(8)
8.7 Conclusion
147(4)
References
147(4)
9 A Hybrid Approach Using The K-Means And Genetic Algorithms For Image Color Quantization
151(22)
Marcos Roberto e Souza
Anderson Carlos Sousa e Santos
Helio Pedrini
9.1 Introduction
151(1)
9.2 Background
152(2)
9.3 Color Quantization Methodology
154(5)
9.3.1 Crossover Operators
157(1)
9.3.2 Mutation Operators
158(1)
9.3.3 Fitness Function
158(1)
9.4 Results and Discussions
159(9)
9.5 Conclusions and Future Work
168(5)
Acknowledgments
168(1)
References
168(5)
Index 173
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.
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