K-medoids clustering is a popular variant of k-means clustering, widely used in pattern recognition and machine learning. However, improper initialization can cause k-medoids clustering to get trapped in local optima. The INCKM algorithm was proposed to overcome this drawback by applying incremental initialization to k-medoids clustering. However, it struggles with imbalanced datasets due to incorrect hyperparameter selection. To address this, the incremental k-means++ (INCKPP) algorithm was developed, which initializes with a novel incremental manner, optimally adding one new cluster center at each stage through a nonparametric and stochastic k-means++ initialization. The INCKPP algorithm improves clustering performance and can handle imbalanced datasets effectively. However, it is not computationally efficient enough. To improve efficiency while maintaining performance, the INCKPPsample algorithm was proposed. Extensive experiments on synthetic and real-world datasets demonstrate that the proposed algorithms outperform other compared algorithms.
k-medoids clustering, k-means++ initialization
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Synthetic and real-world datasets
Clustering performance, efficiency
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No
No
Incremental initialization, improved clustering performance, handles imbalanced datasets
No
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No
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Pattern recognition, machine learning
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No
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No
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No
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0.00
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01/01/1970
01/01/1970
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Yes