paper

The Correspondence Analysis Platform for Uncovering Deep Structure in Data and Information

arXiv:0807.0908 · doi:10.1093/comjnl/bxn045

Abstract

We study two aspects of information semantics: (i) the collection of all relationships, (ii) tracking and spotting anomaly and change. The first is implemented by endowing all relevant information spaces with a Euclidean metric in a common projected space. The second is modelled by an induced ultrametric. A very general way to achieve a Euclidean embedding of different information spaces based on cross-tabulation counts (and from other input data formats) is provided by Correspondence Analysis. From there, the induced ultrametric that we are particularly interested in takes a sequential - e.g. temporal - ordering of the data into account. We employ such a perspective to look at narrative, "the flow of thought and the flow of language" (Chafe). In application to policy decision making, we show how we can focus analysis in a small number of dimensions.

Sixth Annual Boole Lecture in Informatics, Boole Centre for Research in Informatics, Cork, Ireland, 29 April 2008. 28 pp., 17 figures. To appear, Computer Journal. This version: 3 typos corrected

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