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Cluster and Classification Techniques for the Biosciences 1st Edition by Alan H Fielding ISBN 0521618002 9780521618007

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Author(s): Alan H. Fielding
ISBN(s): 9780521618007, 0521618002
Edition: 1
File Details: PDF, 6.88 MB
Year: 2007
Language: english
SKU: EB-1385128 Category: Tag:

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ISBN 10: 0521618002 
ISBN 13: 9780521618007
Author: Alan H Fielding

Advances in experimental methods have resulted in the generation of enormous volumes of data across the life sciences. Hence clustering and classification techniques that were once predominantly the domain of ecologists are now being used more widely. This 2006 book provides an overview of these important data analysis methods, from long-established statistical methods to more recent machine learning techniques. It aims to provide a framework that will enable the reader to recognise the assumptions and constraints that are implicit in all such techniques. Important generic issues are discussed first and then the major families of algorithms are described. Throughout the focus is on explanation and understanding and readers are directed to other resources that provide additional mathematical rigour when it is required. Examples taken from across the whole of biology, including bioinformatics, are provided throughout the book to illustrate the key concepts and each technique’s potential.

Cluster and Classification Techniques for the Biosciences 1st Table of contents:

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
  - Supervised learning
  - Unsupervised learning
  - Machine 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
  - Outline
  - Matrix methods (a very brief review)
  - Example analysis 1
  - Example analysis 2
 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
  - Euclidean metrics
  - Non-Euclidean metrics
  - Semi-metrics
 3.2.2 Importance of data types
  - Distances for interval variables
  - Data transformations
  - Count data
  - Binary data
 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
  - Average linkage clustering
  - Complete linkage clustering
  - Single linkage clustering
  - Within-groups clustering
  - Ward’s method
 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
  - Bacteria data
  - Cancer data

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
  - Signal-to-noise ratio
  - Sequential selection methods
  - Gene expression data
 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
  - Discriminant analysis of two artificial data sets
  - Discriminant analysis of golden eagle data (multi-class analysis)
 5.3.3 Modified algorithms
5.4 Logistic regression
 5.4.1 Introduction
 5.4.2 Example analyses
  - Artificial data
  - Mixed data type analysis
 5.4.3 Cancer data set
  - Background
  - Analysis
  - Residuals and influence statistics
5.5 Discriminant analysis or logistic regression?
5.6 Generalised additive models
 5.6.1 Introduction
 5.6.2 Loess and spline smoothing functions
 5.6.3 Example analysis
5.7 Summary

6. Other Classification Methods

6.1 Background
6.2 Decision trees
 6.2.1 Background
  - Node purity
  - Identifying splits
  - Tree complexity
  - Assigning classes to terminal nodes
  - Missing values
 6.2.2 Example analysis
 6.2.3 Random forests
 6.2.4 Other ‘flavours’
  - ID3, C4.5 and C5
  - CHAID
  - QUEST
  - OC1
6.3 Support vector machines
6.4 Artificial neural networks
 6.4.1 Introduction
 6.4.2 Back-propagation networks
 6.4.3 Modelling general and generalised linear models with neural networks
 6.4.4 Interpreting weights
 6.4.5 Radial bias function networks
 6.4.6 Example analysis
 6.4.7 Self-organising maps
  - Outline
  - Example analysis
6.5 Genetic algorithms
 6.5.1 Introduction
 6.5.2 Genetic algorithms as classifiers
 6.5.3 Feature selection using a GA
6.6 Others
 6.6.1 Case-based reasoning
 6.6.2 Nearest neighbour
 6.6.3 Combined classifiers
6.7 Where next?

7. Classification Accuracy

7.1 Background
7.2 Appropriate metrics
7.3 Binary accuracy measures
7.4 Appropriate testing data
 7.4.1 Re-substitution
 7.4.2 Hold-out
 7.4.3 Cross-validation
 7.4.4 Bootstrapping methods
 7.4.5 Out-of-bag estimates
 7.4.6 Reject rates
7.5 Decision thresholds
7.6 Example
7.7 ROC plots
 7.7.1 Background
 7.7.2 ROC curves
 7.7.3 Comparing classifiers using AUC values
7.8 Incorporating costs
 7.8.1 Costs are universal
 7.8.2 Types of cost
 7.8.3 Using misclassification costs
7.9 Comparing classifiers
7.10 Recommended reading

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Tags: Alan H Fielding, Cluster, Classification Techniques