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Data analysis in bi-partial perspect...

Owsinski, Jan W.

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Data analysis in bi-partial perspective = clustering and beyond /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data analysis in bi-partial perspective/ by Jan W. Owsinski.
Reminder of title:	clustering and beyond /
Author:	Owsinski, Jan W.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xix, 153 p. :ill., digital ;24 cm.
[NT 15003449]:	Preface -- Chapter 1. Notation and main assumptions -- Chapter 2. The problem of cluster analysis -- Chapter 3. The general formulation of the objective function -- Chapter 4. Formulations and rationales for other problems in data analysis, etc.
Contained By:	Springer eBooks
Subject:	Cluster analysis. -
Online resource:	https://doi.org/10.1007/978-3-030-13389-4
ISBN:	9783030133894

Data analysis in bi-partial perspective = clustering and beyond /
Owsinski, Jan W.

Data analysis in bi-partial perspectiveclustering and beyond /[electronic resource] :by Jan W. Owsinski. - Cham :Springer International Publishing :2020. - xix, 153 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.8181860-949X ;. - Studies in computational intelligence ;v.818..

Preface -- Chapter 1. Notation and main assumptions -- Chapter 2. The problem of cluster analysis -- Chapter 3. The general formulation of the objective function -- Chapter 4. Formulations and rationales for other problems in data analysis, etc.

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 representation 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.

ISBN: 9783030133894

Standard No.: 10.1007/978-3-030-13389-4doiSubjects--Topical Terms:

562995
Cluster analysis.

LC Class. No.: QA278.55

Dewey Class. No.: 519.53

Data analysis in bi-partial perspective = clustering and beyond /
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505 0 $a Preface -- Chapter 1. Notation and main assumptions -- Chapter 2. The problem of cluster analysis -- Chapter 3. The general formulation of the objective function -- Chapter 4. Formulations and rationales for other problems in data analysis, etc.
520 $a 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 representation 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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