Clustering Algorithms
Preface
Contents
I. INTRODUCTION
I.1. Clustering
I.1. Examples of Clustering
I.3. Functions of Clustering
I.4. Statistics and Data Analysis
I.5. Types of Data
I.6. Clustering Structures and Models
I.7. Algorithms
I.8. Interpretation and Evaluation. of Clusters
I.9. Using This Book
References
1. PROFILES
1.1. Introduction
1.2. Profiles Algorithm
1.3. Profiles of City Crime
1.4. Rank Profiles Algorithm
1.5. Linearly Optimal Profiles Algorithm
1.6. Linearly Optimal Profiles of Crime Data
1.7. Things To Do
References
Programs
2. DISTANCES
2.1. Introduction
2.2. Euclidean Distances
2.3. Relations Between Variables
2.4. Disguised Euclidean Distances
2.5. Other Distances
2.6. Plotting Distances To Detect Clusters
2.7. Things To Do
References
Programs
3. QUICK PARTITION ALGOR1THMS
3.1. Introduction
3.2. Leader Algorithm
3.3. Leader Algorithm Applied To Jigsaw Puzzle
3.4. Properties of Leader Algorithm
3.5. Sorting Algorithm
3.6. Sorting Algorithm Applied To Jigsaw Puzzle
3.7. Properties of Sorting Algorithm
3.8. Things To Do
Programs
4. THE K-MEANS ALGORITHM
4.1. Introduction
4.2. K-means Algorithm
4.3. K-means Applied To Food Nutrient Data
4.4. Analysis of Variance
4.5. Weights
4.6. Other Distances
4.7. The Shape of K-means Clusters
4.8. Significance Tests
4.9. Things To Do
References
Programs
5. MIXTURES
5.1. Introduction
5.2. Normal Mixture Algorithm
5.3. Normal Mixture Algorithm Applied To New Haven School Scores
5.4. Things To Do
Programs
6. PARTITION BY EXACT OPTIMIZATION
6.1. Introduction
6.2. Fisher Algorithm
6.3. Fisher Algorithm Applied To Olympic Times
6.4. Significance Testing and Stopping Rules
6.5. Time and Space
6.6. Things To Do
References
Programs
7. THE DITTO ALGORITHM
7.1. Introduction
7.2. Ditto Algorithm
7.3. Application of Ditto Algorithm To Wines
7.4. Things To Do
Programs
8. DRAWING TREES
8.1. Definition of a Tree
8.2. Reordering To Contiguous Clusters
8.3. Application of Reordering To Animai Clusters
8.4. Naming Clusters
8.5. I Representation of Clusters With Diameters
8.6. I Representation of Animai Clusters
8.7. Trees and Directed Graphs
8.8. Linear Representations of Trees
8.9. Trees and Distances
8.10. Block Representations of Trees
8.11. Things To Do
References
Programs
9. QUICK TREE CALCULATION
9.1. Introduction
9.2. Leader Algorithm For Trees
9.3. Tree-leader Algorithm Applied To Mammals' Teeth
9.4. Things To Do
Programs
10. TRIADS
10.1. Introduction
10.2. Triads Algorithm
10.3. Triads Algorithm Applied to Hardware
10.4. Properties of Triads Algorithm
10.5. Triads-leader Algorithm
10.6. Application of Triads-leader Algorithm To Expectation of Life
10.7. Remarks On Triads-leader Algorithm
10.8. Things To Do
References
Programs
11. SINGLE-LINKAGE TREES
11.1. Introduction
11.2. Single-linkage Algorithm
11.3. Application of Single-linkage Algorithm To Airline Distances
11.4. Computational Properties of Single Linkage
11.5. Spiral Search Algorithm
11.6. Application of Spiral Search Algorithm To Births and Deaths
11.7. Single-linkage Clusters From Partitions
11.8. Joining and Splitting
11.9. Ultrametrics
11.10. Strung-out Clusters
11.11. Minimum Spanning Trees
11.12. Reality of Clusters
11.13. Density-contour Tree
11.14. Densities and Connectedness, Distances Given
11.15. Things To Do
References
Programs
12. DISTANCE AND AMALGAMATION ALGORITHMS
12.1. Introduction
12.2. Joining Algorithm
12.3. Joining Algorithm Applied To Ivy League Football
12.4. Remarks On Joining Algorithm
12.5. Adding Algorithm
12.6. Adding Algorithm Applied To Questionnaire
12.7. Things To Do
References
Programs
13. MINIMUM MUTATION METHODS
13.1. Introduction
13.2. Minimum Mutation Fits
13.3. Application of Minimum Mutation Algorithm To Cerci In Insects
13.4. Some Probability Theory For the Number of Mutations
13.5. Reduced Mutation Tree
13.6. Application of Reduced Mutation Algorithm To Amino Acid Sequences
13.7. Things To Do
References
Programs
14. DIRECT SPLITTING
14.1. Introduction
14.2. Binary Splitting Algorithm
14.3. Application of Binary Splitting Algorithm To Voting Data With Missing Values
14.4. One-way Splitting Algorithm
14.5. One-way Splitting Algorithm Applied To Republican Percentages
14.6. Two-way Splitting Algorithm
14.7. Two-way Splitting Algorithm Applied To Republican Vote for President
14.8. Things To Do
References
Programs
15. DIRECT JOINING
15.1. lntroduction
15.2. Two-way Joining Algorithm
15.3. ApplicΓ tion of Two-way Joining Algorithm To Candida
15.4. Generalizations of Two-way Joining Algorithm
15.5. Significance Tests for Outcomes of Two-way joining Algorithm
15.6. Ditect Joining Algorithm for Variables on Different Scales
15.7. Things To Do
Programs
16. SIMULTANEOUS CLUSTERING AND SCALING
16.1. Introduction
16.2. Scaling Ordered Variables
16.3. Scaling Ordered Variables Applied To U.N. Questions
16.4. Joiner-scaler
16.5. Application of Joiner-scaler Algorithm To U.N. Votes
16.6. Things To Do
References
17. FACTOR ANALYSIS
17.1. Introduction
17.2. Sparse Root Algorithm
17.3. Sparse Root Algorithm Applied To Face Measurements
17 4. Remarks on the Sparse Root Algorithm
17.5. Rotation to Simple Structure
17.6. Joining Algorithm for Factor Analysis
17.7. Application of Joining Algorithm To Physical Measurements Data
17.8. Things To Do
References
Programs
18. PREDICTION
18.1. Introduction
18.2. Variance Components Algorithm
18.3. Variance Components Algorithm Applied To Prediction of Leukemia Mortality Rates
18.4. Alternatives To Variance Components Algorithm
18.5. Automatic Interaction Detection
18.6. Application of AID Algorithm To Leukemia Mortality
18.7. Remarks On The AID Algorithm
18.8. Things To Do
Programs
INDEX