基于恰当贝叶斯自助法的贝叶斯聚类方法：贝叶斯袋装聚类（BBC）算法
A Bayesian Approach to Clustering via the Proper Bayesian Bootstrap: the Bayesian Bagged Clustering (BBC) algorithm
AI总结：
本文提出贝叶斯袋装聚类（BBC）算法，结合k-means先验引出与恰当贝叶斯自助法重采样的集成聚类思路，提升聚类的鲁棒性与可解释性，还可通过香农熵不确定性度量确定最优聚类数，模拟实验验证了其方法与实证优势。
摘要：
本文提出了一种聚类领域无监督技术的新方法。该方法利用恰当贝叶斯自助法（proper Bayesian bootstrap）改进现有文献中的模型，以提升结果的鲁棒性和可解释性。我们的方法分为两步：先用k-means聚类进行先验引出，再将恰当贝叶斯自助法作为重采样方法应用于集成聚类框架。研究引入基于香农熵的不确定性度量对结果进行分析，该方法既能明确给出最优聚类数，也能更好地呈现聚类后的数据。在模拟数据上的实证结果表明，该方法在方法论和实证层面均取得了进展。
英文摘要：
The paper presents a novel approach for unsupervised techniques in the field of clustering. A new method is proposed to enhance existing literature models using the proper Bayesian bootstrap to improve results in terms of robustness and interpretability. Our approach is organized in two steps: k-means clustering is used for prior elicitation, then proper Bayesian bootstrap is applied as resampling method in an ensemble clustering approach. Results are analyzed introducing measures of uncertainty based on Shannon entropy. The proposal provides clear indication on the optimal number of clusters, as well as a better representation of the clustered data. Empirical results are provided on simulated data showing the methodological and empirical advances obtained.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
