evclust让聚类不仅能分组，还能量化成员归属的不确定性。
evclust: Python library for evidential clustering
- 基于证据理论构建可信分区，表达对象归属的不确定程度。
- 提供高效算法与可视化工具，支持对不确定聚类结果的分析。
- 适合需要评估聚类置信度的研究者或实际应用者。
近年来，聚类算法的发展趋势是不仅识别数据中的簇，还能够表达和捕捉成员归属的不确定性。证据聚类通过德普斯特-沙弗信念函数理论框架来处理和表示不确定性，生成可信分区——一种结构化的质量函数集合，用于量化每个对象对潜在群体的不确定归属。本文提出的Python框架evclust，提供了一套高效的证据聚类算法，以及用于可视化、评估和分析可信分区的工具。
原文摘要 · Abstract (English)
A recent developing trend in clustering is the advancement of algorithms that not only identify clusters within data, but also express and capture the uncertainty of cluster membership. Evidential clustering addresses this by using the Dempster-Shafer theory of belief functions, a framework designed to manage and represent uncertainty. This approach results in a credal partition, a structured set of mass functions that quantify the uncertain assignment of each object to potential groups. The Python framework evclust, presented in this paper, offers a suite of efficient evidence clustering algorithms as well as tools for visualizing, evaluating and analyzing credal partitions.
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