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Non-parameter Clustering Algorithm Based on Chain Propagation and Natural Neighbor

INFORMATION SCIENCES(2024)

Southwest Minzu Univ

Cited 0|Views15
Abstract
Clustering analysis is a powerful tool for discovering potential knowledge in datasets. However, numerous existing clustering algorithms suffer from heavy reliance on parameter settings and cannot cluster complex manifold data very well. Moreover, although non-parameter clustering algorithms aim to lower the usage threshold, their actual performance in clustering is often unsatisfactory. Addressing how to achieve similar clustering effects for numerous data points with only a few core data points during clustering is a valuable consideration. To alleviate these challenges, a non-parameter clustering algorithm is proposed and named Non-parameter Clustering Algorithm based on Chain Propagation and Natural Neighbor(NPCCPN) in this paper, by jointly using chain propagation and natural neighbor. Specifically, NPCCPN identifies core points through chain propagation and clusters them using the saturated neighborhood graph. This makes the core data extraction and clustering process efficient and non-parameter. Finally, the performance of the method is validated on 15 complex synthetic datasets and 10 real datasets from public UCI database. The experimental results show that the effectiveness and superiority of the proposed algorithm. Purity scores first. ACC scores second without parameters, but the score first algorithm needs to set the parameters.
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Key words
Clustering,Non-parameter,Chain propagation,Core data,Natural neighbor
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要点】:本文提出了一种基于链传播和自然邻近的非参数聚类算法(NPCCPN),通过结合链传播和自然邻近方法识别核心点,并使用饱和邻近图进行聚类,以提高核心数据提取和聚类过程的效率,实现了在少量核心数据点的指导下对众多数据点的有效聚类。

方法】:NPCCPN算法通过链传播识别核心点,并利用饱和邻近图进行聚类。

实验】:该方法在15个复杂的合成数据集和10个来自公共UCI数据库的真实数据集上进行了验证。实验结果显示,提出算法的有效性和优越性,其在纯度得分上表现最优,在准确度得分上其次,且无需设置参数。