Clustering for data mining : a data recovery approach

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Clustering for data mining : a data recovery approach / Boris Mirkin

資料種別:: 図書
出版情報:: Boca Raton, Fla. : Chapman & Hall/CRC, Taylor & Francis, 2005
形態:: xxiii, 266 p. ; 25 cm
シリーズ名:: Series in computer science and data analysis ; v. 3 <BA64913131>
著者名:: Mirkin, Boris
ISBN:: 9781584885344 [1584885343] (: hardcover)
書誌ID:: BA71969362

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注記:

Includes bibliographical references (p. 249-259) and index

主題:

データ管理

言語:

英語

目次情報:

Preface

List of Denotations

Introduction: Historical Remarks

What Is Clustering / 1：

Base words

Exemplary problems / 1.1：

Structuring / 1.1.1：

Description / 1.1.2：

Association / 1.1.3：

Generalization / 1.1.4：

Visualization of data structure / 1.1.5：

Bird's-eye view / 1.2：

Definition: data and cluster structure / 1.2.1：

Criteria for revealing a cluster structure / 1.2.2：

Three types of cluster description / 1.2.3：

Stages of a clustering application / 1.2.4：

Clustering and other disciplines / 1.2.5：

Different perspectives of clustering / 1.2.6：

What Is Data / 2：

Feature characteristics / 2.1：

Feature scale types / 2.1.1：

Quantitative case / 2.1.2：

Categorical case / 2.1.3：

Bivariate analysis / 2.2：

Two quantitative variables / 2.2.1：

Nominal and quantitative variables / 2.2.2：

Two nominal variables cross-classified / 2.2.3：

Relation between correlation and contingency / 2.2.4：

Meaning of correlation / 2.2.5：

Feature space and data scatter / 2.3：

Data matrix / 2.3.1：

Feature space: distance and inner product / 2.3.2：

Data scatter / 2.3.3：

Pre-processing and standardizing mixed data / 2.4：

Other table data types / 2.5：

Dissimilarity and similarity data / 2.5.1：

Contingency and flow data / 2.5.2：

K-Means Clustering / 3：

Conventional K-Means / 3.1：

Straight K-Means / 3.1.1：

Square error criterion / 3.1.2：

Incremental versions of K-Means / 3.1.3：

Initialization of K-Means / 3.2：

Traditional approaches to initial setting / 3.2.1：

MaxMin for producing deviate centroids / 3.2.2：

Deviate centroids with Anomalous pattern / 3.2.3：

Intelligent K-Means / 3.3：

Iterated Anomalous pattern for iK-Means / 3.3.1：

Cross validation of iK-Means results / 3.3.2：

Interpretation aids / 3.4：

Conventional interpretation aids / 3.4.1：

Contribution and relative contribution tables / 3.4.2：

Cluster representatives / 3.4.3：

Measures of association from ScaD tables / 3.4.4：

Overall assessment / 3.5：

Ward Hierarchical Clustering / 4：

Agglomeration: Ward algorithm / 4.1：

Divisive clustering with Ward criterion / 4.2：

2-Means splitting / 4.2.1：

Splitting by separating / 4.2.2：

Interpretation aids for upper cluster hierarchies / 4.2.3：

Conceptual clustering / 4.3：

Extensions of Ward clustering / 4.4：

Agglomerative clustering with dissimilarity data / 4.4.1：

Hierarchical clustering for contingency and flow data / 4.4.2：

Data Recovery Models / 4.5：

Statistics modeling as data recovery / 5.1：

Averaging / 5.1.1：

Linear regression / 5.1.2：

Principal component analysis / 5.1.3：

Correspondence factor analysis / 5.1.4：

Data recovery model for K-Means / 5.2：

Equation and data scatter decomposition / 5.2.1：

Contributions of clusters, features, and individual entities / 5.2.2：

Correlation ratio as contribution / 5.2.3：

Partition contingency coefficients / 5.2.4：

Data recovery models for Ward criterion / 5.3：

Data recovery models with cluster hierarchies / 5.3.1：

Covariances, variances and data scatter decomposed / 5.3.2：

Direct proof of the equivalence between 2-Means and Ward criteria / 5.3.3：

Gower's controversy / 5.3.4：

Extensions to other data types / 5.4：

Similarity and attraction measures compatible with K-Means and Ward criteria / 5.4.1：

Application to binary data / 5.4.2：

Agglomeration and aggregation of contingency data / 5.4.3：

Extension to multiple data / 5.4.4：

One-by-one clustering / 5.5：

PCA and data recovery clustering / 5.5.1：

Divisive Ward-like clustering / 5.5.2：

Iterated Anomalous pattern / 5.5.3：

Anomalous pattern versus Splitting / 5.5.4：

One-by-one clusters for similarity data / 5.5.5：

Different Clustering Approaches / 5.6：

Extensions of K-Means clustering / 6.1：

Clustering criteria and implementation / 6.1.1：

Partitioning around medoids PAM / 6.1.2：

Fuzzy clustering / 6.1.3：

Regression-wise clustering / 6.1.4：

Mixture of distributions and EM algorithm / 6.1.5：

Kohonen self-organizing maps SOM / 6.1.6：

Graph-theoretic approaches / 6.2：

Single linkage, minimum spanning tree and connected components / 6.2.1：

Finding a core / 6.2.2：

Conceptual description of clusters / 6.3：

False positives and negatives / 6.3.1：

Conceptually describing a partition / 6.3.2：

Describing a cluster with production rules / 6.3.3：

Comprehensive conjunctive description of a cluster / 6.3.4：

General Issues / 6.4：

Feature selection and extraction / 7.1：

A review / 7.1.1：

Comprehensive description as a feature selector / 7.1.2：

Comprehensive description as a feature extractor / 7.1.3：

Data pre-processing and standardization / 7.2：

Dis/similarity between entities / 7.2.1：

Pre-processing feature based data / 7.2.2：

Data standardization / 7.2.3：

Similarity on subsets and partitions / 7.3：

Dis/similarity between binary entities or subsets / 7.3.1：

Dis/similarity between partitions / 7.3.2：

Dealing with missing data / 7.4：

Imputation as part of pre-processing / 7.4.1：

Conditional mean / 7.4.2：

Maximum likelihood / 7.4.3：

Least-squares approximation / 7.4.4：

Validity and reliability / 7.5：

Index based validation / 7.5.1：

Resampling for validation and selection / 7.5.2：

Model selection with resampling / 7.5.3：

Conclusion: Data Recovery Approach in Clustering / 7.6：

Bibliography

Index

Preface

List of Denotations

Introduction: Historical Remarks

What Is Clustering / 1：

Base words

Exemplary problems / 1.1：

続きを見る

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