论文标题
固定点视图：基于模型的聚类框架
A Fixed point view: A Model-Based Clustering Framework
论文作者
论文摘要
随着数据的充气，作为无监督学习的分支，聚类分析缺乏对其数学定律的统一理解和应用。根据固定点的视图，本文重申了基于模型的聚类并提出了一个统一的聚类框架。为了找到固定点作为群集中心，框架迭代构建收缩图，这强烈揭示了算法之间的收敛机理和互连。通过指定收缩图，可以将高斯混合模型（GMM）映射到框架中作为应用程序。我们希望固定点框架将有助于设计未来的聚类算法。
With the inflation of the data, clustering analysis, as a branch of unsupervised learning, lacks unified understanding and application of its mathematical law. Based on the view of fixed point, this paper restates the model-based clustering and proposes a unified clustering framework. In order to find fixed points as cluster centers, the framework iteratively constructs the contraction map, which strongly reveals the convergence mechanism and interconnections among algorithms. By specifying a contraction map, Gaussian mixture model (GMM) can be mapped to the framework as an application. We hope the fixed point framework will help the design of future clustering algorithms.
