提出一种可自动计算特征与权重的双层多视图模糊聚类方法。
Bi-Level Multi-View fuzzy Clustering with Exponential Distance
- 基于热核系数与权重因子，实现多视图数据聚类。
- 自动同步计算特征重要性与权重，提升聚类精度。
- 适合处理多源异构数据的聚类任务，代码开源。
本研究提出了模糊c均值（FCM）聚类在多视图环境下的扩展方法。首先，提出指数型多视图模糊c均值（E-MVFCM），是一种集中式多视图聚类方法，考虑了热核系数（H-KC）和权重因子。其次，提出指数型双层多视图模糊c均值聚类（EB-MVFCM）。与E-MVFCM不同，EB-MVFCM能同时自动计算特征权重和权重因子。与前者一样，EB-MVFCM显式给出了热核系数，简化了在聚类过程中对热核$K(t)$关于适当时间$ t $的幂次生成。本文所用的所有工具与算法功能均将在https://www.github.com/KristinaP09/EB-MVFCM公开。
原文摘要 · Abstract (English)
In this study, we propose extension of fuzzy c-means (FCM) clustering in multi-view environments. First, we introduce an exponential multi-view FCM (E-MVFCM). E-MVFCM is a centralized MVC with consideration to heat-kernel coefficients (H-KC) and weight factors. Secondly, we propose an exponential bi-level multi-view fuzzy c-means clustering (EB-MVFCM). Different to E-MVFCM, EB-MVFCM does automatic computation of feature and weight factors simultaneously. Like E-MVFCM, EB-MVFCM present explicit forms of the H-KC to simplify the generation of the heat-kernel $\mathcal{K}(t)$ in powers of the proper time $t$ during the clustering process. All the features used in this study, including tools and functions of proposed algorithms will be made available at https://www.github.com/KristinaP09/EB-MVFCM.
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