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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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull?autore
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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Hardcover. Condizione: new. Hardcover. 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 authorsnoted experts on the topicprovide 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 counterpartsOffers an in-depth analysis of a range of optimization algorithmsHighlights a review of data clusteringContains a detailed overview of different standard metaheuristics in current usePresents a step-by-step guide to the build-up of hybrid metaheuristicsOffers 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. "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"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781119551591
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Gebunden. Condizione: New. 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, In. Codice articolo 342236884
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Hardcover. Condizione: new. Hardcover. 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 authorsnoted experts on the topicprovide 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 counterpartsOffers an in-depth analysis of a range of optimization algorithmsHighlights a review of data clusteringContains a detailed overview of different standard metaheuristics in current usePresents a step-by-step guide to the build-up of hybrid metaheuristicsOffers 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. "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"-- Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9781119551591
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ISBN 13: 9781119551591
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Hardcover. Condizione: new. Hardcover. 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 authorsnoted experts on the topicprovide 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 counterpartsOffers an in-depth analysis of a range of optimization algorithmsHighlights a review of data clusteringContains a detailed overview of different standard metaheuristics in current usePresents a step-by-step guide to the build-up of hybrid metaheuristicsOffers 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. "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"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Codice articolo 9781119551591
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