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Clustering Methodology for Symbolic Data
Covers everything readers need to know about clustering methodology for symbolic data—including new methods and headings—while providing a focus on multi-valued list data, interval data and histogram data This book presents all of the latest developments in the field of clustering methodology for symbolic data—paying special attention to the classification methodology for multi-valued list, interval-valued and histogram-valued data methodology, along with numerous worked examples. The book also offers an expansive discussion of data management techniques showing how to manage the large complex dataset into more manageable datasets ready for analyses. Filled with examples, tables, figur...
Advances in Statistical Methods for the Health Sciences
Statistical methods have become an increasingly important and integral part of research in the health sciences. Many sophisticated methodologies have been developed for specific applications and problems. This self-contained comprehensive volume covers a wide range of topics pertaining to new statistical methods in the health sciences, including epidemiology, pharmacovigilance, quality of life, survival analysis, and genomics. The book will serve the health science community as well as practitioners, researchers, and graduate students in applied probability, statistics, and biostatistics.
Understanding Investment Funds
In light of recent financial crises, the role of investment funds is a recurring subject for discussion. Traditional methods must be adapted with the objective to strengthen scientific knowledge of investment funds. This book provides new insights, ideas and empirical evidence to improve tools and methods for fund performance analysis.
Visualization and Verbalization of Data
Visualization and Verbalization of Data shows how correspondence analysis and related techniques enable the display of data in graphical form, which results in the verbalization of the structures in data. Renowned researchers in the field trace the history of these techniques and cover their current applications. The first part of the book explains the historical origins of correspondence analysis and associated methods. The second part concentrates on the contributions made by the school of Jean-Paul Benzécri and related movements, such as social space and geometric data analysis. Although these topics are viewed from a French perspective, the book makes them understandable to an international audience. Throughout the text, well-known experts illustrate the use of the methods in practice. Examples include the spatial visualization of multivariate data, cluster analysis in computer science, the transformation of a textual data set into numerical data, the use of quantitative and qualitative variables in multiple factor analysis, different possibilities of recoding data prior to visualization, and the application of duality diagram theory to the analysis of a contingency table.
Handbook of Cluster Analysis
Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools.The
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Advances in Classification and Data Analysis
The volume presents new developments in classification and mulitivariate analysis. Topics that have been treated with considerable attention are Cluster Analysis, Discriminant Analysis, Proximity Structure Analysis, Multidimensional Scaling, Genetic Algorithms, Neural Networks, Factorial Methods, Textual Data Analysis, Regression Models, Non-parametric Methods, Spatial and Time Series Analysis. Readers will find new methodologies and algorithms in topics that are of central interest in modern statistics, with applications that demonstrate the usefulness of the proposed techniques.
Bioceramics 16
Proceedings of the 16th International Symposium on Ceramics in Medicine, Porto, Portugal, 6-9 November, 2003
Data Analysis, Classification, and Related Methods
The volume presents new developments in data analysis and classification, and gives a state of the art impression of these scientific fields at the turn of the Millennium. Areas that receive considerable attention in this book are Cluster Analysis, Data Mining, Multidimensional and Symbolic Data Analysis, Decision and Regression Trees. The volume contains a refereed selection of original research papers, overview papers, and innovative applications presented at the 7th Conference of the International Federation of Classification Societies (IFCS-2000), with contributions from eminent scientists all over the world. The reader finds introductory material into various areas and kaleidoscopic views of recent technical and methodological developments in widely different areas within data analysis and classification. The presence of a large number of application papers demonstrates the usefulness of the recently developed techniques. TOC:Cluster Analysis.- Discrimination, Regression Trees, and Data Mining.- Multivariate and Multidimensional Data Analysis.- Data Science.- Symbolic Data Analysis.
