Recent Advances in Hybrid Metaheuristics for Data Clustering /
"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"--
| Main Authors: | , , |
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| Format: | text |
| Language: | eng |
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Hoboken, NJ : John Wiley & Sons, Inc.,
2020
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| Subjects: |
| _version_ | 1826470218141007872 |
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| author | De, Sourav, 1979- editor 632324 Dey, Sandip, 1977-, editor 635596 Bhattacharyya, Siddhartha, 1975-, editor 632325 |
| author_facet | De, Sourav, 1979- editor 632324 Dey, Sandip, 1977-, editor 635596 Bhattacharyya, Siddhartha, 1975-, editor 632325 |
| author_sort | De, Sourav, 1979- editor 632324 |
| collection | OCEAN |
| description | "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"-- |
| first_indexed | 2024-03-05T16:44:56Z |
| format | text |
| id | KOHA-OAI-TEST:593153 |
| institution | Universiti Teknologi Malaysia - OCEAN |
| language | eng |
| last_indexed | 2024-03-05T16:44:56Z |
| publishDate | 2020 |
| publisher | Hoboken, NJ : John Wiley & Sons, Inc., |
| record_format | dspace |
| spelling | KOHA-OAI-TEST:5931532023-09-13T00:52:49ZRecent Advances in Hybrid Metaheuristics for Data Clustering / De, Sourav, 1979- editor 632324 Dey, Sandip, 1977-, editor 635596 Bhattacharyya, Siddhartha, 1975-, editor 632325 textHoboken, NJ : John Wiley & Sons, Inc.,2020©2020eng"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"--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.Includes bibliographical references and index.Chapter 1 Metaheuristic Algorithms in Fuzzy Clustering -- Chapter 2 Hybrid Harmony Search Algorithm to Solve the Feature Selection for Data Mining Applications -- Chapter 3 Adaptive Position-Based Crossover in the Genetic Algorithm for Data Clustering -- Chapter 4 Application of Machine Learning in the Social Network -- Chapter 5 Predicting Student’s Grades Using CART, ID3, and Multiclass SVM Optimized by the Genetic Algorithm (GA): A Case Study – Chapter 6 Cluster Analysis of Health Care Data Using Hybrid Nature-Inspired Algorithms – Chapter 7 Performance Analysis Through a Metaheuristic Knowledge Engine – Chapter 8 Magnetic Resonance Image Segmentation Using a Quantum-Inspired Modified Genetic Algorithm (QIANA) Based on FRCM – Chapter 9 A Hybrid Approach Using the k-means and Genetic Algorithms for Image Color Quantization."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"--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.PSZ_JBCluster analysisMetaheuristicsURN:ISBN:9781119551591 |
| spellingShingle | Cluster analysis Metaheuristics De, Sourav, 1979- editor 632324 Dey, Sandip, 1977-, editor 635596 Bhattacharyya, Siddhartha, 1975-, editor 632325 Recent Advances in Hybrid Metaheuristics for Data Clustering / |
| title | Recent Advances in Hybrid Metaheuristics for Data Clustering / |
| title_full | Recent Advances in Hybrid Metaheuristics for Data Clustering / |
| title_fullStr | Recent Advances in Hybrid Metaheuristics for Data Clustering / |
| title_full_unstemmed | Recent Advances in Hybrid Metaheuristics for Data Clustering / |
| title_short | Recent Advances in Hybrid Metaheuristics for Data Clustering / |
| title_sort | recent advances in hybrid metaheuristics for data clustering |
| topic | Cluster analysis Metaheuristics |
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