Generalized Association Plots:
Information Visualization via Iteratively Generated Correlation Matrices
Abstract
1. INTRODUCTION
2. PROXIMITY MATRIX MAP AND SERIATION
2.1 The Proximity Matrix Map
2.2 The Seriation Problem
3. PROPERTIES RELATED TO THE CONVERGENCE PROBLEM
3.1 The p-dimensional Cube and Cone
3.2 Will the sequence Converge ?
3.3 The Decreasing Sequence of Ranks
3.4 The Elliptical Structure Theorem
4. THE GENERAL CONVERGING PATTERNS
4.1 The Rank-One non-symmetry converged matrix
4.2 The Symmetrical Converging structure
5. APPLICATIONS
5.1 The Hierarchical Divisive Clustering Tree with Rank One Spliting Rule.
5.2 The Rank-Two Ellipse Seriation Technique
5.3 Comparison of Seriation Algorithms with Iris Data
5.4 The Perfect Symmetric Structure in Proximity Matrix and the Crystallographic Structure
5.5 The Elliptical Structure with the Eigenvalue Decomposition of the Correlation Matrices
5.6 The Sorted Colored Maps for the Converging Sequence of Correlation Matrices
6. MORE FEATURES OF GAP
6.1 Raw Data and Proximity Matrix Maps with Suitable Color Projection
6.2 The Sorted Matrix Maps with the Principle of Geometry
6.3 Partitioned Matrix Maps with near Stationary Iterations
6.4 The Sufficient Graph with Three Multivariate Linkages
7. DISCUSSION
SOFTWARE