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The validation of the results obtained by clustering algorithms is a fundamental part of the clustering process. The most used approaches for cluster validation are based on internal cluster validity indices. Although many indices have been proposed, there is no recent extensive comparative study of their performance. In this paper we show the results of an(More)
When tourists are at a destination, they typically search for information in the Local Tourist Organizations. There, the staff determines the profile of the tourists and their restrictions. Combining this information with their up-to-date knowledge about the local attractions and public transportation, they suggest a personalized route for the tourist(More)
Class imbalance problems have lately become an important area of study in machine learning and are often solved using intelligent resampling methods to balance the class distribution. The aim of this work is to show that balancing the class distribution is not always the best solution when intelligent resampling methods are used, i.e. there is often a class(More)
The evaluation and comparison of internal cluster validity indices is a critical problem in the clustering area. The methodology used in most of the evaluations assumes that the clustering algorithms work correctly. We propose an alternative methodology that does not make this often false assumption. We compared 7 internal cluster validity indices with both(More)
In the paper a parallelizable system based on Simulated Annealing to solve VRPTW problems is described. The system consists of two optimization phases: a global one, a the local one, both based on Simulated Annealing and paralllizable. For the first phase different parallelization strategies are presented and evaluated. The importance of the cooperation(More)