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The route selection problem has become a hot topic in logistics this few years. How to shorten the route is the key to this point, the traditional algorithms have only consider the length of road. This paper proposed a new algorithm for logistics route selection problem based on clustering and genetic algorithm increases the possibility of gaining the global converging result, and the distance is...
This paper presents a new clustering algorithm based on the combination of GA(Genetic Algorithm) and k-medoids algorithm . The new algorithm can not only improve the precision of clustering but also recognize isolated points. At the same time, the new algorithm may expedite the convergence of GA and save the time cost because of introducing the k-medoids algorithm in GA. Based on this, we build knowledge...
This paper proposes an effective clustering algorithm for databases, which are benchmark data sets of data mining applications. We present a Genetic Clustering Algorithm (GCA) that finds a globally optimal partition of a given data sets into a specified number of clusters. The algorithm is distance-based and creates centroids. To evaluate the proposed algorithm, we use some artificial data sets and...
High-dimensional data clustering is an open problem in modern data mining. This paper proposed a new genetic algorithm-based feature selection for high-dimensional data clustering, called GA-FSFclustering. This approach searches effective feature subsets for clustering in all features by genetic algorithm. The candidate features and cluster centers are real number encoded. A new criterion for evaluating...
A genetic algorithm-based high-dimensional data clustering technique, called GA-HDclustering, is proposed in this paper. This approach searches feature subspace by genetic algorithms to find the effective clustering feature subspaces. The candidate features and cluster centers are binary encoded, and the degree of feature subspace contributes to subspace clustering is proposed as the fitness function...
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