Seriation in Combinatorial and Statistical Data Analysis / by Israël César Lerman, Henri Leredde.

Format:

Book

Author/Creator:

Lerman, Israël César, Author.

Leredde, Henri., Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Advanced information and knowledge processing 2197-8441

Advanced Information and Knowledge Processing, 2197-8441

Language:

English

Subjects (All):

Data mining.

Computer science-Mathematics.

Machine learning.

Data Mining and Knowledge Discovery.

Mathematics of Computing.

Machine Learning.

Local Subjects:

Data Mining and Knowledge Discovery.

Mathematics of Computing.

Machine Learning.

Physical Description:

1 online resource (XIV, 277 pages) : 114 illustrations, 6 illustrations in color.

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

This monograph offers an original broad and very diverse exploration of the seriation domain in data analysis, together with building a specific relation to clustering. Relative to a data table crossing a set of objects and a set of descriptive attributes, the search for orders which correspond respectively to these two sets is formalized mathematically and statistically. State-of-the-art methods are created and compared with classical methods and a thorough understanding of the mutual relationships between these methods is clearly expressed. The authors distinguish two families of methods: Geometric representation methods Algorithmic and Combinatorial methods Original and accurate methods are provided in the framework for both families. Their basis and comparison is made on both theoretical and experimental levels. The experimental analysis is very varied and very comprehensive. Seriation in Combinatorial and Statistical Data Analysis has a unique character in the literature falling within the fields of Data Analysis, Data Mining and Knowledge Discovery. It will be a valuable resource for students and researchers in the latter fields.

Contents:

Preface

Acknowledgements

General Introduction. Methods and History

Seriation from Proximity Variance Analysis

Main Approachs in Seriation. The Attraction Pole Case

Comparing Geometrical and Ordinal Seriation Methods in Formal and Real Cases

A New Family of Combinatorial Algorithms in Seriation

Clustering Methods from Proximity Variance Analysis

Conclusion and Developments.

Other Format:

Printed edition:

ISBN:

978-3-030-92694-6

9783030926946

Access Restriction:

Restricted for use by site license.