Data Analysis in Bi-partial Perspective: Clustering and Beyond

by Jan W. Owsiński
Condition: New
$118.06
VAT included - FREE Shipping
Jan W. Owsiński Data Analysis in Bi-partial Perspective: Clustering and Beyond
Jan W. Owsiński - Data Analysis in Bi-partial Perspective: Clustering and Beyond

Do you like this product? Spread the word!

$118.06 incl. VAT
Only 1 items available Only 1 items available More than 10 pieces available
Delivery: between Tuesday, July 5, 2022 and Thursday, July 7, 2022
Sales & Shipping: Dodax

Description

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.

Contributors

Author:
Jan W. Owsiński

Further information

Illustrations Note:
XIX, 153 p.
Table of Contents:
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.
Remarks:
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 associated with a general objective function


Provides results from studies on data analysis, especially cluster analysis and preference aggregation

Media Type:
Hardcover
Publisher:
Springer International Publishing
Language:
English
Edition:
1st ed. 2020
Number of Pages:
153

Master Data

Product Type:
Hardback book
Release date:
March 23, 2019
Package Dimensions:
0.241 x 0.164 x 0.017 m; 0.413 kg
GTIN:
09783030133887
DUIN:
V6M3L3577ES
$118.06
We use cookies on our website to make our services more efficient and more user-friendly. Therefore please select "Accept cookies"! Please read our Privacy Policy for further information.