9783030133887 - data analysis in bi-partial perspective: clustering and beyond: 818 di owsinski, jan w. (20 risultati)

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Hardcover. Condizione: Brand New. 153 pages. 9.50x6.50x0.75 inches. In Stock.

Lingua: Inglese
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Buch. Condizione: 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 es…sential 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.

Lingua: Inglese
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Serie: Libro 335 di 538 - Studies in Computational Intelligence
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Condizione: 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 represe…ntation 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.

Lingua: Inglese
Editore: Springer, 2019
Serie: Libro 335 di 538 - Studies in Computational Intelligence
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Hardcover. Condizione: gut. 2019. Data Analysis in Bi-partial Perspective: Clustering and Beyond In deutscher Sprache. pages.

Lingua: Inglese
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Lingua: Inglese
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Buch. Condizione: 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 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. 176 pp. Englisch.

Lingua: Inglese
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Serie: Libro 335 di 538 - Studies in Computational Intelligence
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Gebunden. Condizione: 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 associat…ed with a general objective function .

Lingua: Inglese
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Serie: Libro 335 di 538 - Studies in Computational Intelligence
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Buch. Condizione: Neu. Data Analysis in Bi-partial Perspective: Clustering and Beyond | Jan W. Owsi¿ski | Buch | xix | Englisch | 2019 | Springer | EAN 9783030133887 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on… Demand.

Lingua: Inglese
Editore: Springer, Palgrave Macmillan Apr 2019, 2019
Serie: Libro 335 di 538 - Studies in Computational Intelligence
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Buch. Condizione: 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 representa…tion 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.