A fuzzy grade-of-membership (GoM) clustering algorithm is applied to analysis of remote sensing data, in particular, the type of data used in climatic classification. The methodology is applied to a cloud product data subset derived from NASA’s International Satellite Cloud Climatology Project, which includes remotely sensed global monthly average surface temperature and precipitation data for land and coastal regions for the year 1984. GoM partitions for this case are similar to those of vector quantization and fuzzy c-means clustering algorithms, which is significant given the striking differences between the algorithms. The GoM clustering approach is shown to provide an alternative means of interpreting large heterogeneous datasets for exploratory analysis, which broadens the application base by admitting categorical data.

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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.