Foundations and methods in combinatorial and statistical data analysis and clustering
Author(s)
Bibliographic Information
Foundations and methods in combinatorial and statistical data analysis and clustering
(Advanced information and knowledge processing)
Springer, c2016
Available at 2 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
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  Tochigi
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  Saitama
  Chiba
  Tokyo
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  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
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  Gifu
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  Aichi
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  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
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  Tokushima
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  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
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  United Kingdom
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Note
Includes bibliographical references
Description and Table of Contents
Description
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.
Table of Contents
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
by "Nielsen BookData"