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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...
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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