Seriation in Combinatorial and Statistical Data Analysis
Lerman, Israël César.
Seriation in Combinatorial and Statistical Data Analysis [electronic resource] / by Israël César Lerman, Henri Leredde. - 1st ed. 2022. - XIV, 277 p. 114 illus., 6 illus. in color. online resource. - 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.
9783030926946
10.1007/978-3-030-92694-6 doi
Data mining.
Computer science--Mathematics.
Machine learning.
Data Mining and Knowledge Discovery.
Mathematics of Computing.
Machine Learning.
QA76.9.D343
006.312
Seriation in Combinatorial and Statistical Data Analysis [electronic resource] / by Israël César Lerman, Henri Leredde. - 1st ed. 2022. - XIV, 277 p. 114 illus., 6 illus. in color. online resource. - 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.
9783030926946
10.1007/978-3-030-92694-6 doi
Data mining.
Computer science--Mathematics.
Machine learning.
Data Mining and Knowledge Discovery.
Mathematics of Computing.
Machine Learning.
QA76.9.D343
006.312