A methodology is proposed to automatically detect significant symbol associations in genomic databases. A new statistical test is proposed to assess the significance of a group of symbols when found in several genesets of a given database. Applied to symbol pairs, the thresholded p-values of the test define a graph structure on the set of symbols. The cliques of that graph are significant symbo...

Pre-processing or normalisation of data sets is widely used in a number of fields of machine intelligence. Contrary to the overwhelming majority of other normalisation procedures, when data is scaled to a unit range, it is argued in the paper that after normalisation of a data set, the average contributions of all features to the measure employed to assess the similarity of the data have to be ...

Statistical database security is an important problem that has major implications for privacy and protection in the computerised, online world. In this paper we prove that the bounds on the number of answerable range queries in a 1-dimensional statistical database of size n for n odd, or n even and greater than 52, derived in [4] in fact hold for all n except for n = 12, and are achievable usin...

This paper reports on a method for protecting statistical databases against inference, developed over the past four years. The method, called randomizing, is conceptually elegant, easy to implement at very small additional cost, and requires no on-going maintenance. It can be equally applied to small and medium-size as well as large databases, because it does not depend on sampling techniques i...

The machine learning community has focused on confidentiality problems associated with statistical analyses that “integrate” data stored in multiple, distributed databases where there are barriers to simply integrating the databases. This paper discusses various techniques which can be used to perform statistical analysis for categorical data, especially in the form of log-linear analysis and l...

A broad range of image processing applications require image databases during development and testing. Whilst some image databases have been assembled with specific applications in mind, others are intended for more general use, with image content that is purposefully not application-specific. General-purpose image databases are in frequent use in the development of new compression algorithms, ...

One of the main objectives of collecting data in statistical databases (medical databases, census databases) is to find important correlations between different quantities. To enable researchers to looks for such correlations, we should allow them them to ask queries testing different combinations of such quantities. However, when we receive answers to many such questions, we may inadvertently ...

In many areas of science and engineering, it is desirable to estimate statistical characteristics (mean, variance, covariance, etc.) under interval uncertainty. For example, we may want to use the measured values x(t) of a pollution level in a lake at different moments of time to estimate the average pollution level; however, we do not know the exact values x(t) – e.g., if one of the measuremen...

The quest to nd models usefully characterizing data is a process central to the scientiic method, and has been carried out on many fronts. Researchers from an expanding number of elds have designed algorithms to discover rules or equations that capture key relationships between variables in a database. The task of this chapter is to provide a perspective on statistical techniques applicable to ...