Ensemble learning using three-way density-sensitive spectral clustering

聚类分析 相关聚类 CURE数据聚类算法 单连锁聚类 集成学习 模糊聚类 光谱聚类 k-中位数聚类 数据流聚类 树冠聚类算法 模式识别(心理学) 完整的链接聚类 确定数据集中的群集数 火焰团簇 计算机科学 数学 高维数据聚类 数据挖掘 人工智能 算法
作者
Jiachen Fan,Pingxin Wang,Chunmao Jiang,Xibei Yang,Zhen Jin
出处
期刊:International Journal of Approximate Reasoning [Elsevier BV]
卷期号:149: 70-84 被引量:14
标识
DOI:10.1016/j.ijar.2022.07.003
摘要

As one popular clustering algorithm in the last few years, spectral clustering is advantageous over most existing clustering algorithms. Although spectral clustering can perform well in many instances, the algorithm still has some problems. The clusters obtained by spectral clustering have crisp boundaries, which cannot reflect the fact that one cluster may not have a well-defined boundary in the real situations. Furthermore, the frequently-used distance measures in spectral clustering cannot satisfy both global and local consistency, especially for the data with multi-scale. In order to address the above limitations, we firstly present a three-way density-sensitive spectral clustering algorithm, which uses the core region and the fringe region to represent a cluster. In the proposed algorithm, we use density-sensitive distance to produce a similarity matrix, which can well capture the real data structures. An overlap clustering is introduced to obtain the upper bound (unions of the core regions and the fringe regions) of each cluster and perturbation analysis is applied to separate the core regions from the upper bounds. The fringe region of the specific cluster is the differences between the upper bound and the core region. Because a single clustering algorithm cannot always achieve a good clustering result, we develop an improved ensemble three-way spectral clustering algorithm based on ensemble strategy. The proposed ensemble algorithm randomly extracts feature subset of sample and uses the three-way density-sensitive clustering algorithm to obtain the diverse base clustering results. Based on the base clustering results, voting method is used to generate a three-way clustering result. The experimental results show that the three-way density-sensitive clustering algorithm can well explain the data structure and maintain a good clustering performance at the same time, and the ensemble three-way density-sensitive spectral clustering can improve the robustness and stability of clustering results.
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