1. Border-peeling Inspired Globally Central Clustering Algorithm.
- Author
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CHENG Mingchang, AO Lan, and LIU Liu
- Abstract
The globally central clustering algorithms, such as k-means and spectral clustering, often suffer from the problem of local optima and difficulty in parameter setting with overlapping and adhesive clusters in the data distribution, which might greatly limits the effectiveness of globally central clustering algorithms in practical applications. To address this issue, a border-peeling inspired globally central clustering algorithm was proposed. Firstly, a one-step border peeling method was designed, which defines a locally distance-weighted density according to the reverse k-nearest neighbor relationships between sample points. The density value at the maximal point of the first-order difference of the density empirical distribution function was utilized as the threshold to divide the dataset into boundary and core sets. Then, the traditional globally central clustering algorithms were embedded to cluster the core set. Benefiting from the significant improvement in the overlapping of the core set, the embedding algorithms could converge to the true cluster centers easily. Finally, a boundary attraction algorithm was proposed, which could progressively amalgamate sample points from the boundary set, utilizing existing reverse k-nearest neighbor relationships, and commencing from the already categorized core set sample points. Compared with the currently iterative border peeling algorithms, the proposed algorithm had significant advantages in computational efficiency. There was no additional complex termination conditions but only direct performs boundary partitioning using a threshold. Furthermore, the global approach also exhibited stronger robustness local data densities were different. In the experimental phase, three synthetic datasets and six real-world datasets were used to evaluate the algorithm's performance, parameter sensitivity, and time consumption, further validating the efficacy and practicality of this algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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