Data Analysis in Bi-partial Perspective: Clustering and Beyond

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Database Management, Data Processing, General Computing
Cover of the book Data Analysis in Bi-partial Perspective: Clustering and Beyond by Jan W. Owsiński, Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Jan W. Owsiński ISBN: 9783030133894
Publisher: Springer International Publishing Publication: March 23, 2019
Imprint: Springer Language: English
Author: Jan W. Owsiński
ISBN: 9783030133894
Publisher: Springer International Publishing
Publication: March 23, 2019
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer International Publishing

Cover of the book Clastic Hydrocarbon Reservoir Sedimentology by Jan W. Owsiński
Cover of the book Enlargement of Filtration with Finance in View by Jan W. Owsiński
Cover of the book The Decentralized and Networked Future of Value Creation by Jan W. Owsiński
Cover of the book Semantics, Analytics, Visualization. Enhancing Scholarly Data by Jan W. Owsiński
Cover of the book Pierre Musso and the Network Society by Jan W. Owsiński
Cover of the book Information and Communications Security by Jan W. Owsiński
Cover of the book Space Operations: Inspiring Humankind's Future by Jan W. Owsiński
Cover of the book Enterprise and Organizational Modeling and Simulation by Jan W. Owsiński
Cover of the book Decision Support Systems IV - Information and Knowledge Management in Decision Processes by Jan W. Owsiński
Cover of the book AI*IA 2016 Advances in Artificial Intelligence by Jan W. Owsiński
Cover of the book The Molecular Biology of Photorhabdus Bacteria by Jan W. Owsiński
Cover of the book An Introduction to Thermodynamics and Statistical Physics by Jan W. Owsiński
Cover of the book Molecular Mechanisms of Inflammation: Induction, Resolution and Escape by Helicobacter pylori by Jan W. Owsiński
Cover of the book US and EU External Labor Governance by Jan W. Owsiński
Cover of the book The Internet of Things in the Industrial Sector by Jan W. Owsiński
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy