Abstract
Travelling salesman problem (TSP) is an NP-hard optimization problem. So it is necessary to use intelligent and heuristic methods to solve such a hard problem in a less computational time. In this paper, a novel data mining-based approach is presented. The purpose of the proposed approach is to extract a number of rules from optimum tours of small TSPs. The obtained rules can be used for solving larger TSPs. Our proposed approach is mentioned in a standard data mining framework, called CRISP-DM. For rule extracting, generalized rule induction (GRI) as a powerful association rule mining algorithm is used. The results of this approach are stated as if-then rules. This approach is performed on two standard examples of TSPs. The obtained rules from these examples are compared, and it is shown that the rules form two examples have much similarity. This issue shows that it is possible to use from extracted rules to solve larger TSPs.
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Haeri, A., Tavakoli-Moghaddam, R. (2010). Developing a Procedure to Obtain Knowledge of Optimum Solutions in a Travelling Salesman Problem. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_21
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DOI: https://doi.org/10.1007/978-3-642-14831-6_21
Publisher Name: Springer, Berlin, Heidelberg
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