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- W2383922778 abstract "Traditional clustering algorithms such as k-means algorithm require some prior knowledge to determine initial parameters, but the selection of initial parameters usually influences the clustering result greatly. In this paper, a new clustering algorithm based on model is proposed, it finds the best fit of the given data to mathematical model by optimizing the fit between the data and model. GMM can be treated as a “soft assignment clustering” method, and this algorithm learns GMM by combining a greedy EM algorithm which has the ability to learn the GMM structure and parameters automatically without any requirement for prior knowledge. This clustering algorithm can overcome the disadvantage of k-means algorithm and the experiments have been implemented to evaluate the efficiency and performance of the algorithm." @default.
- W2383922778 created "2016-06-24" @default.
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- W2383922778 date "2007-01-01" @default.
- W2383922778 modified "2023-09-25" @default.
- W2383922778 title "A Clustering Algorithm Based on Greedy EM Algorithm Learning GMM" @default.
- W2383922778 hasPublicationYear "2007" @default.
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