01 Applications of Computational Proximity
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02 Applications of Machine Learning
J. F. Peters introduced the concept of near sets, which are disjoint sets containing objects with similar descriptions. Similarity is determined quantitatively via some description of the objects. Near set theory provides a formal basis for identifying, comparing, and measuring resemblance of objects based on their descriptions, i.e. based on the features that describe the objects. The discovery of near sets begins with identifying feature vectors for describing and discerning affinities between sample objects. Objects that have, in some degree, affinities in their features are considered perceptually near each other. Groups of these objects, extracted from the disjoint sets, provide information and reveal patterns of interest.
03 Near Sets
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