基于元素分类连接子图的密度感知层次聚类

中文摘要 AI 辅助

聚类是通过无监督学习进行模式识别的基础数据挖掘技术，在各类聚类方法中，层次聚类、密度聚类和图聚类是代表性方法。层次聚类可分为凝聚式和分裂式两种模式，以递归方式构建聚类，两种模式的关键在于聚类间相似度的计算，该计算决定是否将子聚类合并为一个聚类，或将当前聚类划分为子聚类。传统方法中，相似度由成对距离推导，常忽略图中的密度变化和结构连接性。为解决该问题，本文提出基于元素分类连接子图的密度感知层次聚类方法（DHC-ECS），有效整合了层次聚类、密度聚类和图聚类。特别地，本文引入了一种新颖的聚类间相似度度量，不仅考虑距离，还考虑KNN连接子图中的元素分类、核密度估计以及子聚类内的局部连接性。在异构基准数据集上的大量评估表明，与基线方法（包括AChameleon、RNN-DBSCAN、McDPC和G-RMS）相比，DHC-ECS在聚类准确率和参数鲁棒性方面表现出更优的整体性能。该研究表明，所提出的聚类算法通过利用局部密度和图结构连接性（即顶点与边的对偶性），在低维数据集上具有巨大潜力，同时具备确定内在阈值的可能性，减少了对人工参数调整的依赖。

英文摘要

Clustering is a fundamental data mining technique for pattern recognition through unsupervised learning. Among various clustering methods, hierarchical clustering, density-based clustering, and graph clustering stand out as representative approaches. For hierarchical clustering, it can be categorized into agglomerative and divisive modes to construct clusters in a recursive manner. The key aspect of both modes is the calculation of inter-cluster similarity, which determines whether to merge the sub-clusters into one cluster or divide a current cluster into sub-clusters. Traditionally, the similarity is derived from pairwise distances, often overlooking density variations and structural connectivity in graphs. To address this, we propose a density-aware hierarchical clustering method based on element-categorized connection subgraphs (DHC-ECS), which effectively integrates the hierarchical clustering, density-based clustering, and graph clustering. Particularly, a novel inter-cluster similarity metric is introduced that considers not only distances but also the element categorization in the KNN connection subgraphs, kernel density estimation, and local connectivity within sub-clusters. Extensive evaluations on heterogeneous benchmark datasets demonstrate that DHC-ECS exhibits superior overall performance in terms of clustering accuracy and parameter robustness compared with the baseline methods (including AChameleon, RNN-DBSCAN, McDPC, and G-RMS). The work indicates the great potential of the proposed clustering algorithm for low-dimensional datasets by leveraging local density and graph-structured connectivity (i.e., the duality of vertices and edges), as well as the possibility to determine an intrinsic threshold, reducing the reliance on manual parameter tuning.

发表机构

• Tongji University（同济大学）
• RadioSky (Shanghai) Communication Technology Co., Ltd（天睿（上海）通信技术有限公司）

机构由 AI 辅助整理，请以论文原文为准。