
## 3.3 Conclusion On The Policy Case Studies

The aims and objectives in our use of the Correspondence Analysis and clustering platform is to drive strategy and its implementation in policy.

What we are targeting is to study highly multivariate, evolving data flows.

This is in terms of the semantics of the data - principally, complex webs of interrelationships and evolution of relationships over time. This is the *narrative* of process that lies behind raw statistics and funding decisions.

We have been concerned especially with *information focusing* in section 3.1, and this over time in section 3.2.

## 4 Conclusions

Data collected on processes or configurations can tell us a lot about what is going on. When backed up with probabilistic models, we can test one hypothesis against another. This leads to data mining "in the small". Pairwise plots are often sufficient because as visualizations they remain close and hence faithful to the original data. Such data mining "in the small" is well served by statistics as a discipline.

Separately from such data mining "in the small", there is a need for data mining "in the large". We need to unravel the broad brush strokes of narrative expressing a story. Indeed as discussed in [3], narrative expresses not just a story but human thinking. A broad brush view of science and engineering research, and their linkages with the economy, is a similar type of situation. Pairwise plots are not always sufficient now, because they tie us down in unnecessary detail. We use them since we need the more important patterns, trends, details to be brought out in order of priority. Strategy is of the same class as narrative.

For data mining "in the large" the choice of data and categorization used are crucial. This we have exemplified well in this article. So our data mining "in the large" is tightly coupled to the data. This is how we can make data speak. The modelling involved in analysis "in the small" comes later.

## Appendix: The Correspondence Analysis And Hierarchical Clustering Platform

This Appendix introduces important aspects of Correspondence Analysis and hierarchical clustering. Further reading is to be found in [1] and [7].

## Analysis Chain

1. Starting point: a matrix that cross-tabulates the dependencies, e.g. frequencies of joint occurrence, of an observations crossed by attributes matrix.