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As the amount of information recorded and stored electronically grows ever larger, it becomes increasingly useful, if not essential, to develop better and more efficient ways to summarize and extract information from these large, multivariate data sets. The field of classification does just that-investigates sets of "objects" to see if they can be summarized into a small number of classes comprising similar objects.

Researchers have made great strides in the field over the last twenty years, and classification is no longer perceived as being concerned solely with exploratory analyses. The second edition of Classification incorporates many of the new and powerful methodologies developed since its first edition. Like its predecessor, this edition describes both clustering and graphical methods of representing data, and offers advice on how to decide which methods of analysis best apply to a particular data set. It goes even further, however, by providing critical overviews of recent developments not widely known, including efficient clustering algorithms, cluster validation, consensus classifications, and the classification of symbolic data.

The author has taken an approach accessible to researchers in the wide variety of disciplines that can benefit from classification analysis and methods. He illustrates the methodologies by applying them to data sets-smaller sets given in the text, larger ones available through a Web site.

Large multivariate data sets can be difficult to comprehend-the sheer volume and complexity can prove overwhelming. Classification methods provide efficient, accurate ways to make them less unwieldy and extract more information. Classification, Second Edition offers the ideal vehicle for gaining the background and learning the methodologies-and begin putting these techniques to use.

Introduction

Classification, Assignment, and Dissection

Aims of Classification

Stages in a Numerical Classification

Data Sets

Measures of Similarity and Dissimilarity

Introduction

Selected Measures of Similarity and Dissimilarity

Some Difficulties

Construction of Relevant Measures

Partitions

Partitioning Criteria

Iterative Relocation Algorithms

Mathematical Programming

Other Partitioning Algorithms

How Many Clusters?

Links with Statistical Models

Hierarchical Classifications

Definitions and Representations

Algorithms

Choice of Clustering Strategy

Consensus Trees

More General Tree Models

Other Clustering Procedures

Fuzzy Clustering

Constrained Classification

Overlapping Classification

Conceptual Clustering

Classification of Symbolic Data

Partitions of Partitions

Graphical Representations

Introduction

Principal Coordinates Analysis

Non-Metric Multidimensional Scaling

Interactive Graphics and Self-Organizing Maps

Biplots

Cluster Validation and Description

Introduction

Cluster Validation

Cluster Description

References

Author Index

Subject Index

As the amount of information recorded and stored electronically grows ever larger, it becomes increasingly useful, if not essential, to develop better and more efficient ways to summarize and extract information from these large, multivariate data sets. The field of classification does just that-investigates sets of "objects" to see if they can be summarized into a small number of classes comprising similar objects.

Researchers have made great strides in the field over the last twenty years, and classification is no longer perceived as being concerned solely with exploratory analyses. The second edition of Classification incorporates many of the new and powerful methodologies developed since its first edition. Like its predecessor, this edition describes both clustering and graphical methods of representing data, and offers advice on how to decide which methods of analysis best apply to a particular data set. It goes even further, however, by providing critical overviews of recent developments not widely known, including efficient clustering algorithms, cluster validation, consensus classifications, and the classification of symbolic data.

The author has taken an approach accessible to researchers in the wide variety of disciplines that can benefit from classification analysis and methods. He illustrates the methodologies by applying them to data sets-smaller sets given in the text, larger ones available through a Web site.

Large multivariate data sets can be difficult to comprehend-the sheer volume and complexity can prove overwhelming. Classification methods provide efficient, accurate ways to make them less unwieldy and extract more information. Classification, Second Edition offers the ideal vehicle for gaining the background and learning the methodologies-and begin putting these techniques to use.

Introduction

Classification, Assignment, and Dissection

Aims of Classification

Stages in a Numerical Classification

Data Sets

Measures of Similarity and Dissimilarity

Introduction

Selected Measures of Similarity and Dissimilarity

Some Difficulties

Construction of Relevant Measures

Partitions

Partitioning Criteria

Iterative Relocation Algorithms

Mathematical Programming

Other Partitioning Algorithms

How Many Clusters?

Links with Statistical Models

Hierarchical Classifications

Definitions and Representations

Algorithms

Choice of Clustering Strategy

Consensus Trees

More General Tree Models

Other Clustering Procedures

Fuzzy Clustering

Constrained Classification

Overlapping Classification

Conceptual Clustering

Classification of Symbolic Data

Partitions of Partitions

Graphical Representations

Introduction

Principal Coordinates Analysis

Non-Metric Multidimensional Scaling

Interactive Graphics and Self-Organizing Maps

Biplots

Cluster Validation and Description

Introduction

Cluster Validation

Cluster Description

References

Author Index

Subject Index

As the amount of information recorded and stored electronically grows ever larger, it becomes increasingly useful, if not essential, to develop better and more efficient ways to summarize and extract information from these large, multivariate data sets. The field of classification does just that-investigates sets of "objects" to see if they can be summarized into a small number of classes comprising similar objects.

Researchers have made great strides in the field over the last twenty years, and classification is no longer perceived as being concerned solely with exploratory analyses. The second edition of Classification incorporates many of the new and powerful methodologies developed since its first edition. Like its predecessor, this edition describes both clustering and graphical methods of representing data, and offers advice on how to decide which methods of analysis best apply to a particular data set. It goes even further, however, by providing critical overviews of recent developments not widely known, including efficient clustering algorithms, cluster validation, consensus classifications, and the classification of symbolic data.

The author has taken an approach accessible to researchers in the wide variety of disciplines that can benefit from classification analysis and methods. He illustrates the methodologies by applying them to data sets-smaller sets given in the text, larger ones available through a Web site.

Large multivariate data sets can be difficult to comprehend-the sheer volume and complexity can prove overwhelming. Classification methods provide efficient, accurate ways to make them less unwieldy and extract more information. Classification, Second Edition offers the ideal vehicle for gaining the background and learning the methodologies-and begin putting these techniques to use.

Introduction

Classification, Assignment, and Dissection

Aims of Classification

Stages in a Numerical Classification

Data Sets

Measures of Similarity and Dissimilarity

Introduction

Selected Measures of Similarity and Dissimilarity

Some Difficulties

Construction of Relevant Measures

Partitions

Partitioning Criteria

Iterative Relocation Algorithms

Mathematical Programming

Other Partitioning Algorithms

How Many Clusters?

Links with Statistical Models

Hierarchical Classifications

Definitions and Representations

Algorithms

Choice of Clustering Strategy

Consensus Trees

More General Tree Models

Other Clustering Procedures

Fuzzy Clustering

Constrained Classification

Overlapping Classification

Conceptual Clustering

Classification of Symbolic Data

Partitions of Partitions

Graphical Representations

Introduction

Principal Coordinates Analysis

Non-Metric Multidimensional Scaling

Interactive Graphics and Self-Organizing Maps

Biplots

Cluster Validation and Description

Introduction

Cluster Validation

Cluster Description

References

Author Index

Subject Index

Introduction

Classification, Assignment, and Dissection

Aims of Classification

Stages in a Numerical Classification

Data Sets

Measures of Similarity and Dissimilarity

Introduction

Selected Measures of Similarity and Dissimilarity

Some Difficulties

Construction of Relevant Measures

Partitions

Partitioning Criteria

Iterative Relocation Algorithms

Mathematical Programming

Other Partitioning Algorithms

How Many Clusters?

Links with Statistical Models

Hierarchical Classifications

Definitions and Representations

Algorithms

Choice of Clustering Strategy

Consensus Trees

More General Tree Models

Other Clustering Procedures

Fuzzy Clustering

Constrained Classification

Overlapping Classification

Conceptual Clustering

Classification of Symbolic Data

Partitions of Partitions

Graphical Representations

Introduction

Principal Coordinates Analysis

Non-Metric Multidimensional Scaling

Interactive Graphics and Self-Organizing Maps

Biplots

Cluster Validation and Description

Introduction

Cluster Validation

Cluster Description

References

Author Index

Subject Index

Introduction

Classification, Assignment, and Dissection

Aims of Classification

Stages in a Numerical Classification

Data Sets

Measures of Similarity and Dissimilarity

Introduction

Selected Measures of Similarity and Dissimilarity

Some Difficulties

Construction of Relevant Measures

Partitions

Partitioning Criteria

Iterative Relocation Algorithms

Mathematical Programming

Other Partitioning Algorithms

How Many Clusters?

Links with Statistical Models

Hierarchical Classifications

Definitions and Representations

Algorithms

Choice of Clustering Strategy

Consensus Trees

More General Tree Models

Other Clustering Procedures

Fuzzy Clustering

Constrained Classification

Overlapping Classification

Conceptual Clustering

Classification of Symbolic Data

Partitions of Partitions

Graphical Representations

Introduction

Principal Coordinates Analysis

Non-Metric Multidimensional Scaling

Interactive Graphics and Self-Organizing Maps

Biplots

Cluster Validation and Description

Introduction

Cluster Validation

Cluster Description

References

Author Index

Subject Index

Introduction

Classification, Assignment, and Dissection

Aims of Classification

Stages in a Numerical Classification

Data Sets

Measures of Similarity and Dissimilarity

Introduction

Selected Measures of Similarity and Dissimilarity

Some Difficulties

Construction of Relevant Measures

Partitions

Partitioning Criteria

Iterative Relocation Algorithms

Mathematical Programming

Other Partitioning Algorithms

How Many Clusters?

Links with Statistical Models

Hierarchical Classifications

Definitions and Representations

Algorithms

Choice of Clustering Strategy

Consensus Trees

More General Tree Models

Other Clustering Procedures

Fuzzy Clustering

Constrained Classification

Overlapping Classification

Conceptual Clustering

Classification of Symbolic Data

Partitions of Partitions

Graphical Representations

Introduction

Principal Coordinates Analysis

Non-Metric Multidimensional Scaling

Interactive Graphics and Self-Organizing Maps

Biplots

Cluster Validation and Description

Introduction

Cluster Validation

Cluster Description

References

Author Index

Subject Index