论文标题
通过最小化混合熵标准的适应性聚类
Adaptative clustering by minimization of the mixing entropy criterion
论文作者
论文摘要
我们提出了一种聚类方法，并为自1990年代以来应用统计文献中遇到的现象提供了理论分析和解释。当使用源自著名的EM算法的聚类方法时，这种现象是该阶的自然适应性。我们定义了一个新的统计量，即相对熵顺序，代表目标分布中的团块数量。我们特别证明，这种相对熵顺序的经验版本是一致的。我们的方法易于实施，并且具有很高的应用潜力。这项工作的观点是算法和理论上的，可能对各种情况（例如依赖或多维数据）进行自然扩展。
We present a clustering method and provide a theoretical analysis and an explanation to a phenomenon encountered in the applied statistical literature since the 1990's. This phenomenon is the natural adaptability of the order when using a clustering method derived from the famous EM algorithm. We define a new statistic, the relative entropic order, that represents the number of clumps in the target distribution. We prove in particular that the empirical version of this relative entropic order is consistent. Our approach is easy to implement and has a high potential of applications. Perspectives of this works are algorithmic and theoretical, with possible natural extensions to various cases such as dependent or multidimensional data.
