Co-clustering has emerged as an important technique for mining relational data, especially when data are sparse and high-dimensional. Coclustering simultaneously groups the different kinds of objects involved in a relation. Most co-clustering techniques typically only leverage the entries of the given contingency matrix to perform the two-way clustering. As a consequence, they cannot predict th...

Warpage and shrinkage control are important factors in proving the quality of thin-wall parts in injection modeling process. In the present paper, grey relational analysis was used in order to optimize these two parameters in manufacturing plastic bush of articulated garden tractor. The material used in the plastic bush is Derlin 500. The input parameters in the process were selected according ...

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We present a general formulation of metric learning for co-embedding, where the goal is to relate objects from different sets. The framework allows metric learning to be applied to a wide range of problems—including link prediction, relation learning, multi-label tagging and ranking—while allowing training to be reformulated as convex optimization. For training we provide a fast iterative algor...

In this paper, we examine relational interactions between refugee children and social institutions, building the case for recognition of co-occurrence intertwining vulnerability agency in children’s experiences diverse situations. This developmental approach offers refinement a general worldview by specifying how vulnerable agentic are co-constructed adult individuals institutions. We analyze c...

In the setting of graph-structured data, description logics are well suited to impose constraints that capture the semantics of the domain of interest. When the data evolves as a result of operations carried out by users or applications, it is important to ensure that the satisfaction of the constraints is preserved, analogously to the consistency requirement for transactions in relational data...