一种确定性信息瓶颈方法用于混合类型数据聚类
A Deterministic Information Bottleneck Method for Clustering Mixed-Type Data
AI总结：
本文提出DIBmix方法，通过信息瓶颈原理处理混合类型数据聚类，解决带宽选择和超参数更新问题，在大小不平衡和重叠数据中表现优异。
摘要：
本文提出了一种信息论方法，用于对混合类型数据进行聚类，即包含连续和分类变量的数据。所提出的方法通过广义乘积核将信息瓶颈原理扩展到异质数据，将连续、名义和顺序变量整合到统一的优化框架中。我们解决了以下挑战：开发一种系统带宽选择策略，使不同变量类型的贡献相等，并提出一种自适应超参数更新方案，确保在预定数量的潜在不平衡聚类中获得有效解。通过在28,800个合成数据集和十个公开可用基准上的模拟，我们证明所提出的方法（命名为DIBmix）在四个已建立的方法（KAMILA、K-Prototypes、FAMD与K-Means、PAM与Gower的不相似性）上表现更优。结果表明，DIBmix在聚类大小不平衡、数据包含低或中等聚类重叠以及分类和连续变量等量表示时特别出色。该方法在传统基于质心算法上具有显著优势，建立了DIBmix作为混合类型数据聚类的有竞争力且理论坚实的替代方法。
英文摘要：
In this paper, we present an information-theoretic method for clustering mixed-type data, that is, data consisting of both continuous and categorical variables. The proposed approach extends the Information Bottleneck principle to heterogeneous data through generalised product kernels, integrating continuous, nominal, and ordinal variables within a unified optimization framework. We address the following challenges: developing a systematic bandwidth selection strategy that equalises contributions across variable types, and proposing an adaptive hyperparameter updating scheme that ensures a valid solution into a predetermined number of potentially imbalanced clusters. Through simulations on 28,800 synthetic data sets and ten publicly available benchmarks, we demonstrate that the proposed method, named DIBmix, achieves superior performance compared to four established methods (KAMILA, K-Prototypes, FAMD with K-Means, and PAM with Gower's dissimilarity). Results show DIBmix particularly excels when clusters exhibit size imbalances, data contain low or moderate cluster overlap, and categorical and continuous variables are equally represented. The method presents a significant advantage over traditional centroid-based algorithms, establishing DIBmix as a competitive and theoretically grounded alternative for mixed-type data clustering.
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。
