Author: Erhard Godehardt
Publisher: Springer Science & Business Media
ISBN: 3322963101
Category : Mathematics
Languages : en
Pages : 224
Book Description
The advent of the high-speed computer with its enormous storage capabilities enabled statisticians as well as researchers from the different topics of life sciences to apply mul tivariate statistical procedures to large data sets to explore their structures. More and more, methods of graphical representation and data analysis are used for investigations. These methods belong to a topic of growing popUlarity, known as "exploratory data analysis" or EDA. In many applications, there is reason to believe that a set of objects can be clus tered into subgroups that differ in meaningful ways. Extensive data sets, for example, are stored in clinical cancer registers. In large data sets like these, nobody would ex pect the objects to be homogeneous. The most commonly used terms for the class of procedures that seek to separate the component data into groups are "cluster analysis" or "numerical taxonomy". The origins of cluster analysis can be found in biology and anthropology at the beginning of the century. The first systematic investigations in cluster analysis are those of K. Pearson in 1894. The search for classifications or ty pologies of objects or persons, however, is indigenous not only to biology but to a wide variety of disciplines. Thus, in recent years, a growing interest in classification and related areas has taken place. Today, we see applications of cluster analysis not only to. biology but also to such diverse areas as psychology, regional analysis, marketing research, chemistry, archaeology and medicine.
Graphs as Structural Models
Author: Erhard Godehardt
Publisher: Springer Science & Business Media
ISBN: 3322963101
Category : Mathematics
Languages : en
Pages : 224
Book Description
The advent of the high-speed computer with its enormous storage capabilities enabled statisticians as well as researchers from the different topics of life sciences to apply mul tivariate statistical procedures to large data sets to explore their structures. More and more, methods of graphical representation and data analysis are used for investigations. These methods belong to a topic of growing popUlarity, known as "exploratory data analysis" or EDA. In many applications, there is reason to believe that a set of objects can be clus tered into subgroups that differ in meaningful ways. Extensive data sets, for example, are stored in clinical cancer registers. In large data sets like these, nobody would ex pect the objects to be homogeneous. The most commonly used terms for the class of procedures that seek to separate the component data into groups are "cluster analysis" or "numerical taxonomy". The origins of cluster analysis can be found in biology and anthropology at the beginning of the century. The first systematic investigations in cluster analysis are those of K. Pearson in 1894. The search for classifications or ty pologies of objects or persons, however, is indigenous not only to biology but to a wide variety of disciplines. Thus, in recent years, a growing interest in classification and related areas has taken place. Today, we see applications of cluster analysis not only to. biology but also to such diverse areas as psychology, regional analysis, marketing research, chemistry, archaeology and medicine.
Publisher: Springer Science & Business Media
ISBN: 3322963101
Category : Mathematics
Languages : en
Pages : 224
Book Description
The advent of the high-speed computer with its enormous storage capabilities enabled statisticians as well as researchers from the different topics of life sciences to apply mul tivariate statistical procedures to large data sets to explore their structures. More and more, methods of graphical representation and data analysis are used for investigations. These methods belong to a topic of growing popUlarity, known as "exploratory data analysis" or EDA. In many applications, there is reason to believe that a set of objects can be clus tered into subgroups that differ in meaningful ways. Extensive data sets, for example, are stored in clinical cancer registers. In large data sets like these, nobody would ex pect the objects to be homogeneous. The most commonly used terms for the class of procedures that seek to separate the component data into groups are "cluster analysis" or "numerical taxonomy". The origins of cluster analysis can be found in biology and anthropology at the beginning of the century. The first systematic investigations in cluster analysis are those of K. Pearson in 1894. The search for classifications or ty pologies of objects or persons, however, is indigenous not only to biology but to a wide variety of disciplines. Thus, in recent years, a growing interest in classification and related areas has taken place. Today, we see applications of cluster analysis not only to. biology but also to such diverse areas as psychology, regional analysis, marketing research, chemistry, archaeology and medicine.
Graphs As Structural Models
Author: Erhard Godehardt
Publisher:
ISBN: 9783322963116
Category :
Languages : en
Pages : 228
Book Description
Publisher:
ISBN: 9783322963116
Category :
Languages : en
Pages : 228
Book Description
Structural Models
Author: Robert Zane Norman
Publisher:
ISBN:
Category : Social sciences
Languages : en
Pages : 415
Book Description
Publisher:
ISBN:
Category : Social sciences
Languages : en
Pages : 415
Book Description
Structural Models in Anthropology
Author: Per Hage
Publisher:
ISBN:
Category : Social Science
Languages : en
Pages : 238
Book Description
Structural analysis in the social sciences has an extensive history. Frequently, however, it has been undertaken largely on the basis of intuition and common sense alone. In this book Per Hage and Frank Harary reveal the deeper insights into social and cultural structures that can be obtained through the application of graph theory.
Publisher:
ISBN:
Category : Social Science
Languages : en
Pages : 238
Book Description
Structural analysis in the social sciences has an extensive history. Frequently, however, it has been undertaken largely on the basis of intuition and common sense alone. In this book Per Hage and Frank Harary reveal the deeper insights into social and cultural structures that can be obtained through the application of graph theory.
Structural Models: an Introduction Tothe Theory of Directed Graphs
Application of Directed Graphs as Structural Models
Author: Hooi-Tong Loh
Publisher:
ISBN:
Category : Graph theory
Languages : en
Pages :
Book Description
Publisher:
ISBN:
Category : Graph theory
Languages : en
Pages :
Book Description
Structural Models
Exponential Random Graph Models for Social Networks
Author: Dean Lusher
Publisher: Cambridge University Press
ISBN: 0521193567
Category : Business & Economics
Languages : en
Pages : 361
Book Description
This book provides an account of the theoretical and methodological underpinnings of exponential random graph models (ERGMs).
Publisher: Cambridge University Press
ISBN: 0521193567
Category : Business & Economics
Languages : en
Pages : 361
Book Description
This book provides an account of the theoretical and methodological underpinnings of exponential random graph models (ERGMs).
Structural models
Graph Structure and Monadic Second-Order Logic
Author: Bruno Courcelle
Publisher: Cambridge University Press
ISBN: 1139644009
Category : Mathematics
Languages : en
Pages : 743
Book Description
The study of graph structure has advanced in recent years with great strides: finite graphs can be described algebraically, enabling them to be constructed out of more basic elements. Separately the properties of graphs can be studied in a logical language called monadic second-order logic. In this book, these two features of graph structure are brought together for the first time in a presentation that unifies and synthesizes research over the last 25 years. The authors not only provide a thorough description of the theory, but also detail its applications, on the one hand to the construction of graph algorithms, and, on the other to the extension of formal language theory to finite graphs. Consequently the book will be of interest to graduate students and researchers in graph theory, finite model theory, formal language theory, and complexity theory.
Publisher: Cambridge University Press
ISBN: 1139644009
Category : Mathematics
Languages : en
Pages : 743
Book Description
The study of graph structure has advanced in recent years with great strides: finite graphs can be described algebraically, enabling them to be constructed out of more basic elements. Separately the properties of graphs can be studied in a logical language called monadic second-order logic. In this book, these two features of graph structure are brought together for the first time in a presentation that unifies and synthesizes research over the last 25 years. The authors not only provide a thorough description of the theory, but also detail its applications, on the one hand to the construction of graph algorithms, and, on the other to the extension of formal language theory to finite graphs. Consequently the book will be of interest to graduate students and researchers in graph theory, finite model theory, formal language theory, and complexity theory.