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- W2617366479 abstract "Since K-means clustering algorithm is easy to implement and high efficient, it has been widely used in cluster analysis of massive datasets. The value of k is difficult to determine in advance and the randomness of choosing initial centers leads to a series of social problems, such as instability, local optimal solution sensitivity to outliers. Results from hierarchical clustering are more natural than those from K-means clustering, but its high time complexity and space complexity makes it difficult to be applied to a large data set. In this paper, through combination of hierarchical clustering and K-means clustering, we have proposed an improved K-means clustering algorithm, and have done experiments using datasets provided by MovieLens." @default.
- W2617366479 created "2017-06-05" @default.
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- W2617366479 date "2017-01-01" @default.
- W2617366479 modified "2023-09-23" @default.
- W2617366479 title "An Improved K-means Clustering Algorithm Applicable to Massive High-dimensional Matrix Datasets" @default.
- W2617366479 doi "https://doi.org/10.1051/itmconf/20171104001" @default.
- W2617366479 hasPublicationYear "2017" @default.
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