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Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering de Israël César Lerman

Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering: Advanced Information and Knowledge Processing

Autor Israël César Lerman
en Limba Engleză Paperback – 14 apr 2018
This book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field.
With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial  and  statistical.
Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages:
  • Clustering a set of descriptive attributes
  • Clustering a set of objects or a set of object categories
  • Establishing correspondence between these two dual clusterings
Tools for interpreting the reasons of a given cluster or clustering are also included.
Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery.
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Specificații

ISBN-13: 9781447173922
ISBN-10: 1447173929
Ilustrații: XXIV, 647 p. 54 illus.
Dimensiuni: 155 x 235 mm
Greutate: 0.93 kg
Ediția:Softcover reprint of the original 1st ed. 2016
Editura: SPRINGER LONDON
Colecția Springer
Seria Advanced Information and Knowledge Processing

Locul publicării:London, United Kingdom

Cuprins

Preface.- On Some Facets of the Partition Set of a Finite Set.- Two Methods of Non-hierarchical Clustering.- Structure and Mathematical Representation of Data.- Ordinal and Metrical Analysis of the Resemblance Notion.- Comparing Attributes by a Probabilistic and Statistical Association I.- Comparing Attributes by a Probabilistic and Statistical Association II.- Comparing Objects or Categories Described by Attributes.- The Notion of “Natural” Class, Tools for its Interpretation. The Classifiability Concept.- Quality Measures in Clustering.- Building a Classification Tree.- Applying the LLA Method to Real Data.- Conclusion and Thoughts for Future Works

Recenzii

“This book provides a synthetic and systematic presentation of clustering, combinatorial, and statistical data analysis. … the presentation is interesting and original. Keeping a smart balance between theoretical concepts and practical issues, the book is addressed to students and researchers interested in data mining, data analysis, and clustering.” (Florin Gorunescu, zbMATH 1338.62012, 2016)

Textul de pe ultima copertă

This book offers an original and broad exploration of the fundamental methods in Clustering and Combinatorial Data Analysis, presenting new formulations and ideas within this very active field.
With extensive introductions, formal and mathematical developments and real case studies, this book provides readers with a deeper understanding of the mutual relationships between these methods, which are clearly expressed with respect to three facets: logical, combinatorial  and  statistical.
Using relational mathematical representation, all types of data structures can be handled in precise and unified ways which the author highlights in three stages:
  • Clustering a set of descriptive attributes
    • Clustering a set of objects or a set of object categories
  • Establishing correspondence between these two dual clusterings
Tools for interpreting the reasons of a given cluster or clustering are also included.
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Foundations and Methods in Combinatorial and Statistical Data Analysis and Clustering will be a valuable resource for students and researchers who are interested in the areas of Data Analysis, Clustering, Data Mining and Knowledge Discovery.

Caracteristici

Offers a step-by-step process of the path of the data to the synthetic structure summarizing the data given by a hierarchical or non-hierarchical clustering Presents brand new principles and methods within the Data Mining field Examines ascendant agglomerative hierarchical clustering and Likelihood Linkage Analysis (LLA) clustering methods from metrical, algorithmic and computational aspects Includes supplementary material: sn.pub/extras