Partitional Clustering via Nonsmooth Optimization Clustering via Optimization /
This updated book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering p...
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| Autores principales: | , , |
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| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Lenguaje: | inglés |
| Publicado: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2025.
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| Edición: | 2nd ed. 2025. |
| Colección: | Unsupervised and Semi-Supervised Learning,
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| Materias: | |
| Acceso en línea: | https://doi.org/10.1007/978-3-031-76512-4 |
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| 100 | 1 | |a Bagirov, Adil. |e author. |0 (orcid)0000-0003-2075-1699 |1 https://orcid.org/0000-0003-2075-1699 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Partitional Clustering via Nonsmooth Optimization |h [electronic resource] : |b Clustering via Optimization / |c by Adil Bagirov, Napsu Karmitsa, Sona Taheri. |
| 250 | |a 2nd ed. 2025. | ||
| 264 | 1 | |a Cham : |b Springer Nature Switzerland : |b Imprint: Springer, |c 2025. | |
| 300 | |a 1 online resource (XX, 395 p. 100 illus.). |b 99 illus. in color. | ||
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| 490 | 1 | |a Unsupervised and Semi-Supervised Learning, |x 2522-8498 | |
| 505 | 0 | |a Introduction -- Introduction to Clustering -- Clustering Algorithms -- Nonsmooth Optimization Models in Cluster Analysis -- Nonsmooth Optimization -- Optimization based Clustering Algorithms -- Implementation and Numerical Results -- Conclusion. | |
| 520 | |a This updated book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering problems; the authors' emphasis on clustering algorithms is based on deterministic methods of optimization. The book also includes results on real-time clustering algorithms based on optimization techniques, addresses implementation issues of these clustering algorithms, and discusses new challenges arising from very large data and data with noise and outliers. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the field and it is well suited for practitioners already familiar with the basics of optimization. Designed for a typical undergraduate, graduate, or dual-listed course with a semester-based calendar; Puts theory in context, so readers gain knowledge about the most essential concepts and algorithms; Covers essential terms, algorithms, and useful tools for learning and performing contemporary AI. | ||
| 588 | |a Print version record. | ||
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| 650 | 2 | 4 | |a Automated Pattern Recognition. |
| 650 | 2 | 4 | |a Signal, Speech and Image Processing. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 650 | 2 | 4 | |a Data Mining and Knowledge Discovery. |
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| 700 | 1 | |a Taheri, Sona. |e author. |0 (orcid)0000-0003-1779-4567 |1 https://orcid.org/0000-0003-1779-4567 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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| 830 | 0 | |a Unsupervised and Semi-Supervised Learning, |x 2522-8498 | |
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