9783030133887 - data analysis in bi-partial perspective: clustering and beyond: 818 (studies in computational intelligence, 818) de owsiński, jan w. (18 resultados)
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents thebi-partial approachto data analysis, which is both uniquely general and enables the development of techniques for many data analysis problems, including related models and algorithms. It is based on adequate representation of the ess…ential clustering problem:to group together the similar, and to separate the dissimilar. This leads to a general objective function and subsequently to a broad class of concrete implementations. Using this basis, a suboptimising procedure can be developed, together with a variety of implementations.This procedure has a striking affinity with the classical hierarchical merger algorithms, while also incorporating the stopping rule, based on the objective function. The approach resolves the cluster number issue, as the solutions obtained include both the content and the number of clusters. Further, it is demonstrated how the bi-partial principle can be effectively applied to a wide variety of problems in data analysis.The book offers a valuable resource for all data scientists who wish to broaden their perspective on basic approaches and essential problems, and to thus find answers to questions that are often overlooked or have yet to be solved convincingly. It is also intended for graduate students in the computer and data sciences, and will complement their knowledge and skills with fresh insights on problems that are otherwise treated in the standard 'academic' manner.
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Hardcover. Condición: Brand New. 153 pages. 9.50x6.50x0.75 inches. In Stock.
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Serie: Libro 335 de 538 - Studies in Computational Intelligence
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Condición: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This book presents the bi-partial approach to data analysis, which is both uniquely general and enables the development of techniques for many data analysis problems, including related models and algorithms. It is based on adequate represen…tation of the essential clustering problem: to group together the similar, and to separate the dissimilar. This leads to a general objective function and subsequently to a broad class of concrete implementations. Using this basis, a suboptimising procedure can be developed, together with a variety of implementations.This procedure has a striking affinity with the classical hierarchical merger algorithms, while also incorporating the stopping rule, based on the objective function. The approach resolves the cluster number issue, as the solutions obtained include both the content and the number of clusters. Further, it is demonstrated how the bi-partial principle can be effectively applied to a wide variety of problems in data analysis. The book offers a valuable resource for all data scientists who wish to broaden their perspective on basic approaches and essential problems, and to thus find answers to questions that are often overlooked or have yet to be solved convincingly. It is also intended for graduate students in the computer and data sciences, and will complement their knowledge and skills with fresh insights on problems that are otherwise treated in the standard ¿academic¿ manner.
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Hardcover. Condición: gut. 2019. Data Analysis in Bi-partial Perspective: Clustering and Beyond In deutscher Sprache. pages.
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Serie: Libro 335 de 538 - Studies in Computational Intelligence
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Condición: new. Questo è un articolo print on demand.
Idioma: Inglés
Editorial: Springer International Publishing Apr 2019, 2019
Serie: Libro 335 de 538 - Studies in Computational Intelligence
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents thebi-partial approachto data analysis, which is both uniquely general and enables the development of techniques for many data analysis problems, including related models and algorithms. It is based on adequate represent…ation of the essential clustering problem:to group together the similar, and to separate the dissimilar. This leads to a general objective function and subsequently to a broad class of concrete implementations. Using this basis, a suboptimising procedure can be developed, together with a variety of implementations.This procedure has a striking affinity with the classical hierarchical merger algorithms, while also incorporating the stopping rule, based on the objective function. The approach resolves the cluster number issue, as the solutions obtained include both the content and the number of clusters. Further, it is demonstrated how the bi-partial principle can be effectively applied to a wide variety of problems in data analysis.The book offers a valuable resource for all data scientists who wish to broaden their perspective on basic approaches and essential problems, and to thus find answers to questions that are often overlooked or have yet to be solved convincingly. It is also intended for graduate students in the computer and data sciences, and will complement their knowledge and skills with fresh insights on problems that are otherwise treated in the standard 'academic' manner. 176 pp. Englisch.
Idioma: Inglés
Editorial: Springer International Publishing, 2019
Serie: Libro 335 de 538 - Studies in Computational Intelligence
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Librería: moluna, Greven, Alemaniamoluna
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Offers a valuable resource for all data scientists who wish to broaden their perspective on the fundamental approaches available Presents a general formulation, properties, examples, and techniques associate…d with a general objective function .
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Editorial: Springer, 2019
Serie: Libro 335 de 538 - Studies in Computational Intelligence
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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Idioma: Inglés
Editorial: Springer, Palgrave Macmillan Apr 2019, 2019
Serie: Libro 335 de 538 - Studies in Computational Intelligence
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book presents the bi-partial approach to data analysis, which is both uniquely general and enables the development of techniques for many data analysis problems, including related models and algorithms. It is based on adequate representat…ion of the essential clustering problem: to group together the similar, and to separate the dissimilar. This leads to a general objective function and subsequently to a broad class of concrete implementations. Using this basis, a suboptimising procedure can be developed, together with a variety of implementations.This procedure has a striking affinity with the classical hierarchical merger algorithms, while also incorporating the stopping rule, based on the objective function. The approach resolves the cluster number issue, as the solutions obtained include both the content and the number of clusters. Further, it is demonstrated how the bi-partial principle can be effectively applied to a wide variety of problems in data analysis.The book offers a valuable resource for all data scientists who wish to broaden their perspective on basic approaches and essential problems, and to thus find answers to questions that are often overlooked or have yet to be solved convincingly. It is also intended for graduate students in the computer and data sciences, and will complement their knowledge and skills with fresh insights on problems that are otherwise treated in the standard 'academic' manner.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 176 pp. Englisch.
