6.4.1 Implementation: Equivalence of Iterative Approximation and Batch Calculation -- 6.5 Inducing a Hierarchical Clustering from Seriation -- 6.6 Short Summary of All These Methodological Underpinnings -- 6.6.1 Trivial First Eigenvalue, Eigenvector in Correspondence Analysis -- 6.7 Very High-Dimensional Data Spaces: Data Piling -- 6.8 Recap on Correspondence Analysis for Following Applications -- 6.8.1 Clouds of Points, Masses and Inertia -- 6.8.2 Relative and Absolute Contributions -- 6.9 Evaluation 1: Uniformly Distributed Data Cloud Points -- 6.9.1 Computation Time Requirements -- 6.10 Evaluation 2: Time Series of Financial Futures -- 6.11 Evaluation 3: Chemistry Data, Power Law Distributed -- 6.11.1 Data and Determining Power Law Properties -- 6.11.2 Randomly Generating Power Law Distributed Data in Varying Embedding Dimensions -- 6.12 Application 1: Quantifying Effectiveness through Aggregate Outcome -- 6.12.1 Computational Requirements, from Original Space and Factor Space Identities -- 6.13 Application 2: Data Piling as Seriation of Dual Space -- 6.14 Brief Concluding Summary -- 6.15 Annex: R Software Used in Simulations and Evaluations -- 6.15.1 Evaluation 1: Dense, Uniformly Distributed Data -- 6.15.2 Evaluation 2: Financial Futures -- 6.15.3 Evaluation 3: Chemicals of Specified Marginal Distribution -- IV: New Frontiers: New Vistas on Information, Cognition and the Human Mind -- 7: On Ultrametric Algorithmic Information -- 7.1 Introduction to Information Measures -- 7.2 Wavelet Transform of a Set of Points Endowed with an Ultrametric -- 7.3 An Object as a Chain of Successively Finer Approximations -- 7.3.1 Approximation Chain using a Hierarchy -- 7.3.2 Dendrogram Wavelet Transform of Spherically Complete Space -- 7.4 Generating Faces: Case Study Using a Simplified Model -- 7.4.1 A Simplified Model of Face Generation