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Self -paced Learning for K -means Clustering Algorithm
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DOI：10.1016/j.patrec.2018.08.028.png)
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The traditional K-means clustering algorithm is easily affected by the noise, outliers and falling into local optimal solution. This paper proposes a K-means clustering algorithm based on self-paced learning. Firstly, a best training subset is selected to construct the initial cluster model base on self-paced learning theory, and then enhances the generalization ability of the initial clustering model by adding sub-good subsets of samples one by one until the model performance is optimal or all training samples are used up. By analyzing the experimental results, the clustering algorithm proposed in this paper achieves better performance than the compare algorithms on the five real data sets. (c) 2018 Elsevier B.V. All rights reserved.
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