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And when we want to scale up the way to do that is through componentized small data: by creating and integrating small data "packages" not building big data monoliths, by partitioning problems in a way that works across people and organizations, not through creating massive centralized silos.
The main graph clustering formulations are based on graph cut and partitioning problems [39, 40].
Currently, the two-exchange neighborhood is the most widely used neighborhood for solving partitioning problems.
Like other partitioning problems this one is also an NP-hard.
Both binary and extended partitioning problems are constrained optimization problems and are NP-hard.
The relational encoding eliminates the redundancy of previous GA representations for partitioning problems and improves the performance of genetic search.
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Partitioning problem is an NP-hard problem.
Therefore, the bargaining model is attractive for the partitioning problem.
We also propose a heuristic solution to the partitioning problem.
One of these key issues is the partitioning problem.
Another well-studied graph partitioning problem is the k-cut problem.
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