基于最小γ-散度的自发聚类
Spontaneous Clustering via Minimum γ-divergence
摘要：
我们提出了一种新的聚类方法的基础上的局部最小化的γ-散度，我们称之为自发聚类。所提出的方法的最大优点是，它可以自动检测的集群，充分反映了数据结构的数量。相比之下，现有的方法，如K-means，模糊C-均值，基于模型的聚类需要规定的聚类数。我们检测所有的局部极小点的γ-散度，这被定义为聚类的中心。在一个简单的条件下，得到了γ-散度存在局部极小点的一个充分必要条件.仿真研究和实际数据分析进行比较，我们的建议与现有的方法。
英文摘要：
We propose a new method for clustering based on the local minimization of the γ-divergence, which we call the spontaneous clustering. The greatest advantage of the proposed method is that it automatically detects the number of clusters that adequately reflect the data structure. In contrast, exiting methods such as K-means, fuzzy c-means, and model based clustering need to prescribe the number of clusters. We detect all the local minimum points of the γ-divergence, which are defined as the centers of clusters. A necessary and sufficient condition for the γ-divergence to have the local minimum points is also derived in a simple setting. A simulation study and a real data analysis are performed to compare our proposal with existing methods.
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。
