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In this paper, we study a new problem of continuous learning from doubly-streaming data where both data volume and feature space increase over time. We refer to the doubly-streaming data as trapezoidal data streams and the corresponding learning problem as online learning from trapezoidal data streams. The problem is challenging because both data volume and data dimension increase over time, and existing...
Rough concept lattice (RCL) is a new concept lattice, which can deal with uncertainty knowledge. It is an effective tool for data analysis and knowledge discovery. But some objects that in upper approximation extent of RCL are so uncertainty that makes the accuracy of association rules reduced. In this case, based on decision context, we construct a L- plumpness concept lattice (Lp - RCL), in whose...
Given a tree T = (V, E) with costs defined on edges, a positive integer k, and I terminal sets {S1, S2, . . ., Sl} with every Si sube V, the generalized k-multicut in trees problem (k-GMC(T)) asks to find an edge subset in E at the minimum cost such that its removal cuts at least k terminal sets. The k-GMC(T) problem is a natural generalization of the classical multicut in trees problem and the multiway...
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