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Springer Foundations and Methods in Combinatorial Data Analysis - Springer

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Description

This comprehensive guide explores fundamental methods in clustering and combinatorial data analysis, presenting original formulations and ideas in this active field. Written for researchers and practitioners, the book combines extensive introductions with formal mathematical developments and real case studies to reveal the mutual relationships between clustering techniques. Using relational mathematical representation, the work handles all types of data structures with precision and unified approaches. The content is structured around three essential facets - logical, combinatorial, and statistical - providing readers with deeper insights into how these perspectives interact. Ideal for computer scientists, data analysts, and machine learning professionals seeking rigorous theoretical foundations alongside practical applications in modern data analysis.

Key Features

The book presents original formulations and new ideas within the active field of clustering and combinatorial data analysis.

Extensive introductions paired with formal mathematical developments enable readers to grasp complex relationships between different analytical methods.

Real case studies throughout demonstrate how combinatorial and statistical data analysis techniques apply to practical problems and datasets.

Relational mathematical representation allows the methods to handle all types of data structures with precision and consistency.

The content explores three interconnected facets - logical, combinatorial, and statistical - revealing how each perspective contributes to modern data analysis.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
14 April 2018
Listed Since
14 April 2018

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