This paper describes a technique for estimating business opportunity metrics from mixed numeric and categorical type databases by using a fuzzy Grade-of-Membership clustering model. The technique is applied to the problem of opportunity analysis for business decision-making. We propose two metrics called unfamiliarity and follow-on importance. Real business contract data are used to demonstrate the technique. This general approach could be adapted to many other applications where a decision agent needs to assess the value of items from a set of opportunities with respect to a reference set representing its business.

A general purpose reversible memory-binding transform (MBT) is developed, which uses a permutation transform technique to bind memory information to a transformed signal alphabet. The algorithm performs well in conjunction with a Huffman coder for both ordered sources, such as pixel intensities, and categorical sources, such as vector quantized codebook indices.

Many scientific applications employ computer graphics as a means of interactively visualizing two-dimensional geophysical data sets. This paper presents an algorithm for improving rendering performance by using an image segmentation approach to reduce the number of rendered polygons while maintaining complete display fidelity. Though applied here specifically to the output from a numerical ionosphere model, the algorithm is broadly applicable to many other two-dimensional cellular data sets, geophysical and otherwise. In an ionospheric context, this algorithm demonstrates the ability to reduce the number of rendered polygons by 95 per cent or more with a corresponding reduction in rendering time.