Seriation in Combinatorial and Statistical Data Analysis

This monograph explores the seriation domain in data analysis, building a specific relation to clustering. It offers original methods and compares them to classical methods, providing a thorough understanding of their mutual relationships. The experimental analysis is diverse and comprehensive, making it a valuable resource for students and researchers in Data Analysis, Data Mining, and Knowledge Discovery.

Format: Hardback
Length: 277 pages
Publication date: 05 March 2022
Publisher: Springer Nature Switzerland AG

This comprehensive monograph delves into the realm of seriation in data analysis, offering a novel and diverse exploration of this domain. It establishes a specific connection with clustering, formalizing the search for orders that correspond to a data table crossing a set of objects and a set of descriptive attributes. State-of-the-art methods are developed and compared with classical approaches, providing a clear understanding of the mutual relationships between these methods. The authors categorize two families of methods: Geometric representation methods and Algorithmic and Combinatorial methods. Within these families, original and accurate methods are presented, with a basis and comparison made on both theoretical and experimental levels. The experimental analysis is extensive and comprehensive, covering a wide range of scenarios.

Seria in Combinatorial and Statistical Data Analysis stands out in the literature as a unique contribution to Data Analysis, Data Mining, and Knowledge Discovery. It will be an invaluable resource for students and researchers in these fields.

Weight: 606g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030926939
Edition number: 1st ed. 2022