Gregory Vert

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K-means is a popular clustering algorithm that requires a huge initial set to start the clustering. K-means is an unsupervised clustering method which does not guarantee convergence. Numerous improvements to K-means have been done to make its performance better. Expectation Maximization is a statistical technique for maximum likelihood estimation using(More)
Current work in modeling has focused on fuzziness as it applies to single entities. An application of fuzzy theory may be made to managing data sets and collections, thus treating the data sets as collection of fuzzy objects. The application of fuzzy theory to management of sets creates has not been fully explored. In the area of Geospatial Information(More)
DNA matching is a crucial step in sequence alignment. Since sequence alignment is an approximate matching process there is a need for good approximate algorithms. The process of matching in sequence alignment is generally finding longest common subsequences. However, finding a longest common subsequence may not be the best solution for either a database(More)
Spatial data has become more important everyday in decision-making and planning processes. As such, it needs to be stored and retrieved in information systems that often require high performance due to the voluminous nature of spatial data. Typically this is not much of a problem unless one considers the effect of spatial extent as a function of time in(More)
In this paper, we introduce a new paradigm for global computation, one in which the context of collected information drives the type of processing and dissemination the information receives as it is dispersed around the world. The creation of this model has necessitated the development of new types of methods for securing contextual information because the(More)