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Seriation in combinatorial and stati...

Lerman, Israel Cesar.

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Seriation in combinatorial and statistical data analysis

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Seriation in combinatorial and statistical data analysis/ by Israel Cesar Lerman, Henri Leredde.
作者:	Lerman, Israel Cesar.
其他作者:	Leredde, Henri.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xiv, 277 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Data mining - Statistical methods. -
電子資源:	https://doi.org/10.1007/978-3-030-92694-6
ISBN:	9783030926946

Seriation in combinatorial and statistical data analysis
Lerman, Israel Cesar.

Seriation in combinatorial and statistical data analysis[electronic resource] /by Israel Cesar Lerman, Henri Leredde. - Cham :Springer International Publishing :2022. - xiv, 277 p. :ill. (some col.), digital ;24 cm. - Advanced information and knowledge processing,2197-8441. - Advanced information and knowledge processing..

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.

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.

ISBN: 9783030926946

Standard No.: 10.1007/978-3-030-92694-6doiSubjects--Topical Terms:

576356
Data mining
--Statistical methods.

LC Class. No.: QA76.9.D343 / L47 2022

Dewey Class. No.: 006.312

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505 0 $a 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.
520 $a 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.
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