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• W2895654611 abstract "We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters. Additionally, the initial identification of the cluster elements has no negative impact on the final generated clusters. Inspired by cell cloning in microorganism cultures, each added data sample causes the existing cluster ‘colonies’ to evaluate, with the other clusters, various merging or splitting actions in order for reaching the optimum cluster set. The proposed algorithm is adequate for clustering data in isolated or overlapped compact spherical clusters. Experimental results support the effectiveness of this clustering algorithm." @default.
• W2895654611 created "2018-10-12" @default.
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• W2895654611 date "2018-10-06" @default.
• W2895654611 modified "2023-10-18" @default.
• W2895654611 title "K-Means Cloning: Adaptive Spherical K-Means Clustering" @default.
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• W2895654611 doi "https://doi.org/10.3390/a11100151" @default.
• W2895654611 hasPublicationYear "2018" @default.
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