Graphs as Structural Models The Application of Graphs and Multigraph
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 structu
- PDF / 25,905,669 Bytes
- 224 Pages / 481.89 x 691.654 pts Page_size
- 2 Downloads / 200 Views
Graphs as Structural Models
Advances in System Analysis Editor: Dietmar P F. Mbller
Volume 1: Emil S. Bucherl (Ed.) Proceedings of the Second World Symposium Artificial Heart Volume 2: Dietmar P. F. Moller (Ed.) System Analysis of Biological Processes Volume 3: Kiichi Tsuchiya and Mitsuo Umezu Mechanical Simulator of the Cardiovascular System: Design, Development and Application Volume 4: Erhard Godehardt Graphs as Structural Models
Manuscripts submitted to Advances in System Analysis must be original, pointing out the advancement of the contribution with respect to the actual a-priori knowledge. Manuscripts or exposes should be sent to the Editor of the Series:
Dietmar P. F. Moller, Johannes Gutenberg Universitat Mainz, Physiologisches Institut, Saarstr. 21, D-6500 Mainz 1, W.-Germany.
Erhard Godehardt
Graphs as Structural Models The Application of Graphs and Multigraphs in Cluster Analysis
B
Jt-----~D
Springer Fachmedien Wiesbaden GmbH Braunschweig I Wiesbaden
CIP-Titelaufnahme der Deutschen Bibliothek
GOdehardt, Erhard: Graphs as structural models: the application of graphs and multigraphs in cluster analysis/ Erhard Godehardt. - Braunschweig; Wiesbaden: Vieweg, 1988 (Advances in system analysis; VoI. 4) ISBN 978-3-528-06312-2 ISBN 978-3-322-96310-9 (eBook) DOI 10.1007/978-3-322-96310-9 NE:GT
Vieweg is a subsidiary company of the Bertelsmann Publishing Group. AII rights reserved ©Springer Fachmedien Wiesbaden, Braunschweig 1988 Ursprunglich erschienen bei Friedr. Vieweg & Sohn Verlagsgesellschaft 1988 No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical photocopying, recording or otherwise, without prior of permission of the copyright holder.
ISBN 978-3-528-06312-2
ISSN
0932-593X
v
PREFACE 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 multivariate 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 clustered 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 expect 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 typologies of objects or persons, however, is indigenous not only to biology but to a wide variety of
