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Stream clustering methods, which group continuous, temporally ordered dynamic data instances, have been used in a number of applications such as stock market analysis, network analysis, and cosmological analysis. Most of the popular stream clustering algorithms are linear in nature, i.e. they assume that the data is linearly separable in the input space and use measures such as the Euclidean distance...
Investigations that require the exploitation of large volumes of face imagery are increasingly common in current forensic scenarios (e.g., Boston Marathon bombing), but effective solutions for triaging such imagery (i.e., low importance, moderate importance, and of critical interest) are not available in the literature. General issues for investigators in these scenarios are a lack of systems that...
The ubiquity of personal computing technology has produced an abundance of staggeringly large data sets — the Library of Congress has stored over 160 terabytes of web data and it is estimated that Facebook alone logs over 25 terabytes of data per day. There is a great need for systems by which one can elucidate the similarity and dissimilarity among and between groups in these data sets. Clustering...
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