.png){kind=icon}
返回
An improved density peak clustering algorithm guided by pseudo labels
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2022.109374.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Density peak clustering algorithms and their variants have achieved promising results in many fields over the last few years. However, most of these algorithms parameters requiring to be fine-tuned by users. When facing real-world data without ground-truths, it is often challenging and time-consuming to identify better parameter values for parametric clustering algorithms. Considering this, we propose a density peak clustering algorithm guided by pseudo labels (PLDPC), in which the manually prespecified parameters are avoided through applying the mutual information criterion. Specifically, we first design a novel pseudo-label generation method based on the theory of co-occurrence. Then, we use the maximizing mutual information method to obtain better clustering results. To evaluate the effectiveness of the proposed PLDPC algorithm, we conduct extensive experiments on 23 datasets, including six synthetic and seventeen real-world datasets. The experimental results show that PLDPC outperforms three classical algorithms (i.e., K-means, DPC, and DBSCAN) and eight state-of-the-art (SOTA) clustering algorithms in most cases. (c) 2022 Published by Elsevier B.V.
Keyword:
Density peak clustering
Pseudo labels
Maximizing mutual information
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
The Effect of Loving-Kindness Meditation on Flight Attendants’ Spirituality, Mindfulness and Subjective Well-Being.png)
Healthcare
OAAI
HealthcareIF0
OAAI分享收藏Study on density peaks clustering based on k-nearest neighbors and principal component analysis基于k近邻和主成分分析的密度峰聚类研究
PREAI
分享收藏Density peaks clustering based on density backbone and fuzzy neighborhoodPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
分享收藏
查看更多内容