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Initialization of fuzzy k-means algorithm decreases the convergent rate of clustering and leads to plenty of calculation. Thus, we propose an improved fuzzy k-means clustering based on k-center algorithm and binary tree in this paper, which firstly reduces redundant attributes while too many irrespective attributes affect the efficiency of clustering. Secondly, we remove the differences of units of...
Present, there is more research on supervised clustering ensemble algorithm, but the research on unsupervised clustering ensemble is studied less. In order to partition data points under fully unsupervised conditions, the hierarchical clustering ensemble algorithm based on association rules (HCEAR) is proposed in this paper. The optimal number of clusters is determined by average degree of clustering...
The variables of organic matter, available N, available P and available K data determined in 193 topsoil (0-30 cm) samples were selected as data sources. Fuzzy c-means clustering algorithm was used to delineate management zones. In order to determine the optimum fuzzy control parameters, the fuzziness performance index (FPI), c-?? combinations and the multiple regression based on external variable...
Existing clustering ensemble algorithms for partitioning categorical data only apply to know the generating process of clustering members very well. In order to broaden the application of clustering ensemble, a fuzzy clustering ensemble algorithm for partitioning categorical data is proposed in this paper. The proposed algorithm makes use of relationship degree between different attributes for pruning...
Web services have been considered as an effective method to create unprecedented opportunities for organizations to establish more agile and versatile collaborations with other organizations. But services dynamic discovery is one of factors not only tiring consumers but also preventing them from enjoying high quality of service. It is one of key issues in services dynamic discovery how to select the...
Traditional k-means algorithm can make the distances of objects in the same cluster as small as possible, but the distances of objects from different clusters are not satisfied efficiently and usually the dataset with mixed numeric and categorical data is not classified correctly. The IWEKM (improved weight entropy k-means) algorithm is proposed in this paper. The proposed algorithm overcomes the...
The objective of traditional k-means algorithm is to make the distances of objects in the same cluster as small as possible, but another objective that the distances of objects from different clusters is not taken into account. This paper presents an improved k-means algorithm satisfying both of objectives above. We modify the cost function of entropy weighting k-means clustering algorithm by adding...
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