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.