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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 book is organized according to the traditional
core approaches to cluster analysis, from the origins to recent
developments. After an overview of approaches and a quick journey
through the history of cluster analysis, the book focuses on the
four major approaches to cluster analysis. These approaches include
methods for optimizing an objective function that describes how
well data is grouped around centroids, dissimilarity-based methods,
mixture models and partitioning models, and clustering methods
inspired by nonparametric density estimation. The book also
describes additional approaches to cluster analysis, including
constrained and semi-supervised clustering, and explores other
relevant issues, such as evaluating the quality of a cluster. This
handbook is accessible to readers from various disciplines,
reflecting the interdisciplinary nature of cluster analysis. For
those already experienced with cluster analysis, the book offers a
broad and structured overview. For newcomers to the field, it
presents an introduction to key issues. For researchers who are
temporarily or marginally involved with cluster analysis problems,
the book gives enough algorithmic and practical details to
facilitate working knowledge of specific clustering areas.
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 book is organized according to the traditional
core approaches to cluster analysis, from the origins to recent
developments. After an overview of approaches and a quick journey
through the history of cluster analysis, the book focuses on the
four major approaches to cluster analysis. These approaches include
methods for optimizing an objective function that describes how
well data is grouped around centroids, dissimilarity-based methods,
mixture models and partitioning models, and clustering methods
inspired by nonparametric density estimation. The book also
describes additional approaches to cluster analysis, including
constrained and semi-supervised clustering, and explores other
relevant issues, such as evaluating the quality of a cluster. This
handbook is accessible to readers from various disciplines,
reflecting the interdisciplinary nature of cluster analysis. For
those already experienced with cluster analysis, the book offers a
broad and structured overview. For newcomers to the field, it
presents an introduction to key issues. For researchers who are
temporarily or marginally involved with cluster analysis problems,
the book gives enough algorithmic and practical details to
facilitate working knowledge of specific clustering areas.
This volume contains a selection of papers presented at the
biannual meeting of the Classification and Data Analysis Group of
Societa Italiana di Statistica, which was held in Rome, July 5-6,
1999. From the originally submitted papers, a careful review
process led to the selection of 45 papers presented in four parts
as follows: CLASSIFICATION AND MULTIDIMENSIONAL SCALING Cluster
analysis Discriminant analysis Proximity structures analysis and
Multidimensional Scaling Genetic algorithms and neural networks MUL
TIV ARIA TE DATA ANALYSIS Factorial methods Textual data analysis
Regression Models for Data Analysis Nonparametric methods SPATIAL
AND TIME SERIES DATA ANALYSIS Time series analysis Spatial data
analysis CASE STUDIES INTERNATIONAL FEDERATION OF CLASSIFICATION
SOCIETIES The International Federation of Classification Societies
(IFCS) is an agency for the dissemination of technical and
scientific information concerning classification and data analysis
in the broad sense and in as wide a range of applications as
possible; founded in 1985 in Cambridge (UK) from the following
Scientific Societies and Groups: British Classification Society
-BCS; Classification Society of North America - CSNA; Gesellschaft
fUr Klassifikation - GfKI; Japanese Classification Society -JCS;
Classification Group of Italian Statistical Society - CGSIS;
Societe Francophone de Classification -SFC. Now the IFCS includes
also the following Societies: Dutch-Belgian Classification Society
- VOC; Polish Classification Society -SKAD; Associayao Portuguesa
de Classificayao e Analise de Dados -CLAD; Korean Classification
Society -KCS; Group-at-Large.
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