聚类分析
模糊逻辑
人工智能
模糊聚类
模式识别(心理学)
计算机科学
数学
数据挖掘
作者
Abdulrahman Lotfi,Parham Moradi,Hamid Beigy
标识
DOI:10.1016/j.patcog.2020.107449
摘要
Abstract Density peaks clustering (DPC) is as an efficient clustering algorithm due for using a non-iterative process. However, DPC and most of its improvements suffer from the following shortcomings: (1) highly sensitive to its cutoff distance parameter, (2) ignoring the local structure of data in computing local densities, (3) using a crisp kernel to calculate local densities, and (4) suffering from the cause of chain reaction. To address these issues, in this paper a new method called DPC-DBFN is proposed. The proposed method uses a fuzzy kernel for improving separability of clusters and reducing the impact of outliers. DPC-DBFN uses a density-based kNN graph for labeling backbones. This strategy prevents the chain reaction and effectively assigns true labels to those instances located on the border regions to effectively cluster data with various shapes and densities. The DPC-DBFN is evaluated on some real-world and synthetic datasets. The experimental results show the effectiveness and robustness of the proposed algorithm.
科研通智能强力驱动
Strongly Powered by AbleSci AI