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Finding interestingness measures to evaluate association rules has become an important knowledge quality issue in KDD. Many interestingness measures may be found in the literature, and many authors have discussed and compared interestingness properties in order to improve the choice of the most suitable measures for a given application. As interestingness(More)
We present a diversity-oriented hybrid evolutionary approach for the graph coloring problem. This approach is based on both generally applicable strategies and specifically tailored techniques. Particular attention is paid to ensuring population diversity by carefully controlling spacing among individuals. Using a distance measure between potential(More)
We present a search space analysis and its application in improving local search algorithms for the graph coloring problem. Using a classical distance measure between colorings, we introduce the following clustering hypothesis: the high quality solutions are not randomly scattered in the search space, but rather grouped in clusters within spheres of(More)
This paper presents a new stochastic heuristic to reveal some structures inherent in large graphs, by displaying spatially separate clusters of highly connected vertex subsets on a two-dimensional grid. The algorithm employed is inspired by a biological model of ant behavior; it proceeds by local optimisations, and requires neither global criteria, nor any(More)
We present a hybrid evolutionary algorithm for the graph coloring problem (Evocol). Evocol is based on two simple-but-effective ideas. First, we use an enhanced crossover that collects the best color classes out of more than two parents; the best color classes are selected using a ranking based on both class fitness and class size. We also introduce a(More)