Notes:

Includes bibliographical references and index.

Cover -- Half-title -- Title -- Copyright -- Dedication -- Contents
-- Preface -- 1 Introduction -- 1.1 Background -- 1.2 Book structure
-- 1.3 Classification -- 1.4 Clustering -- 1.5 Structures in data --
1.5.1 Structure in tables -- 1.5.2 Graphical identification of
structure -- 1.6 Glossary -- 1.6.1 Algorithm -- 1.6.2 Bias -- 1.6.3
Deviance -- 1.6.4 Learning -- 1.6.5 Maximum likelihood estimation --
1.6.6 Microarray -- 1.6.7 Multicollinearity -- 1.6.8 Occam's razor --
1.6.9 Ordination -- 1.7 Recommended reading and other resources --
1.7.1 Books -- 1.7.2 Software -- 2 Exploratory data analysis -- 2.1
Background -- 2.2 Dimensionality -- 2.3 Goodness of fit testing --
2.4 Graphical methods -- 2.4.1 Background -- 2.5 Variance-based data
projections -- 2.5.1 Background -- 2.5.2 PCA -- 2.5.3 Factor analysis
-- 2.6 Distance-based data projections -- 2.6.1 Background -- 2.6.2
MDS or principal coordinate analysis -- 2.6.3 Sammon mapping -- 2.6.4
Non-metric multidimensional scaling (NMDS) -- 2.7 Other projection
methods -- 2.7.1 Correspondence analysis -- 2.7.2 Canonical
correspondence analysis -- 2.8 Other methods -- 2.8.1 Mantel tests --
2.8.2 Procrustes rotation -- 2.9 Data dredging -- 2.10 Example EDA
analysis -- 3 Cluster analysis -- 3.1 Background -- 3.2 Distance and
similarity measures -- 3.2.1 Distance measures -- 3.2.2 Importance of
data types -- 3.2.3 Other distance measures -- 3.3 Partitioning
methods -- 3.3.1 k-means -- 3.3.2 k-medians and PAM -- 3.3.3 Mixture
models -- 3.3.4 Others -- 3.4 Agglomerative hierarchical methods --
3.4.1 Joining clusters: clustering algorithms -- 3.4.2 The dendrogram
-- 3.5 How many groups are there? -- 3.5.1 Scree plots -- 3.5.2 Other
methods of estimating optimum number of clusters -- 3.6 Divisive
hierarchical methods -- 3.7 Two-way clustering and gene shaving --
3.8 Recommended reading -- 3.9 Example analyses -- 3.9.1 Hierarchical
clustering of bacterial strains -- 3.9.2 Hierarchical clustering of
the human genus -- 3.9.3 Partition clustering -- 4 Introduction to
classification -- 4.1 Background -- 4.2 Black-box classifiers -- 4.3
Nature of a classifier -- 4.4 No-free-lunch -- 4.5 Bias and variance
-- 4.6 Variable (feature) selection -- 4.6.1 Background -- 4.6.2
Variable selection methods -- 4.6.3 Ranking the importance of
predictors -- 4.7 Multiple classifiers -- 4.7.1 Background -- 4.7.2
Boosting and bagging -- 4.7.3 Combining different classifiers -- 4.8
Why do classifiers fail? -- 4.9 Generalisation -- 4.10 Types of
classifier -- 5 Classification algorithms 1 -- 5.1 Background -- 5.2
Naïve Bayes -- 5.3 Discriminant analysis -- 5.3.1 Introduction --
5.3.2 Example analyses -- 5.3.3 Modified algori

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Classification:

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Dewey Class: 570.285 -- Life sciences
LCC Number: QH324.2

Book Details:

Language: eng
Physical Description: xii, 246 p. : ill. ; 26 cm.
Edition Info: (electronic bk.)

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