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- W2381799794 abstract "Traditional K-means clustering analysis does not consider the different objects in each variable to reflect the role of the clustering process,but a unified look at the distance calculated in this way to represent the similarity between two objects is not exact.This paper presents a clustering algorithm based on distance metric,the algorithm uses a new distance metric instead of Euclidean distance of the K-means clustering algorithm,and apply the new distance measure,the data points in the right weight is no longer only for 1 or 0,while determined by the coefficient,which will be hard to divide into soft division.The experiments show that the improved clustering algorithm has been greatly improved convergence rate than the traditional K-means clustering to improve the efficiency of the implementation of the algorithm." @default.
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- W2381799794 date "2012-01-01" @default.
- W2381799794 modified "2023-09-23" @default.
- W2381799794 title "An improved distance metric clustering algorithm" @default.
- W2381799794 hasPublicationYear "2012" @default.
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