Data Analysis in Bi-partial Perspective: Clustering and Beyond (Hardcover, 1st ed. 2020)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Studies in Computational Intelligence, 818
Release date:	March 2019
First published:	2020
Authors:	Jan W. Owsinski
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	153
Edition:	1st ed. 2020
ISBN-13:	978-3-03-013388-7
Categories:	Books > Computing & IT > Applications of computing > Databases > General Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-013388-5
Barcode:	9783030133887

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