Graphs Structural Models by Erhard Godehardt (12 results)

Broschiert. Condition: Gut. 2. ed. 239 Seiten; Der Erhaltungszustand des hier angebotenen Werks ist trotz seiner Bibliotheksnutzung sehr sauber. Es befindet sich neben dem Rückenschild lediglich ein Bibliotheksstempel im Buch; ordnungsgemäß entwidmet. In ENGLISCHER Sprache. Sprache: Englisch Gewicht in Gramm: 370.

Softcover. Condition: Fine. The emergence of high-speed computers with vast storage capabilities has allowed statisticians and life science researchers to apply multivariate statistical procedures to large data sets, facilitating the exploration of their structures. Increasingly, graphical representation and data analysis methods are employed in investigations, falling under the growing field of exploratory data analysis (EDA). Many applications suggest that objects can be meaningfully clustered into subgroups. For instance, extensive data sets in clinical cancer registers are unlikely to be homogeneous. The procedures aimed at separating data into groups are known as cluster analysis or numerical taxonomy. The roots of cluster analysis trace back to biology and anthropology in the early 20th century, with K. Pearson's systematic investigations in 1894 marking a significant milestone. The quest for classifications or typologies is not limited to biology; it spans various disciplines. Recently, there has been an increasing interest in classification and related areas, leading to applications of cluster analysis across diverse fields such as psychology, regional analysis, marketing research, chemistry, archaeology, and medicine.

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Condition: New. Series: Advances in System Analysis. BIC Classification: PBV. Dimension: 0 x 0. Weight in Grams: 401. . 1988. Paperback. . . . .

Condition: New. Series: Advances in System Analysis. BIC Classification: PBV. Dimension: 0 x 0. Weight in Grams: 401. . 1988. Paperback. . . . . Books ship from the US and Ireland.

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 214 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Straße 46, 65189 Wiesbaden 228 pp. Englisch.

Taschenbuch. Condition: Neu. Graphs as Structural Models | The Application of Graphs and Multigraphs in Cluster Analysis | Erhard Godehardt | Taschenbuch | x | Englisch | 1988 | Vieweg & Teubner | EAN 9783528063122 | Verantwortliche Person für die EU: Springer Vieweg in Springer Science + Business Media, Abraham-Lincoln-Str. 46, 65189 Wiesbaden, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.