This paper studies clustering of data sequences using the k-medoids algorithm. All the data sequences are assumed to be generated from \emph{unknown} continuous distributions, which form clusters with each cluster containing a composite set of closely located distributions (based on a certain distance metric between distributions). The maximum intra-cluster distance is assumed to be smaller than the minimum inter-cluster distance, and both values are assumed to be known. The goal is to group the data sequences together if their underlying generative distributions (which are unknown) belong to one cluster. Distribution distance metrics based k-medoids algorithms are proposed for known and unknown number of distribution clusters. Upper bounds on the error probability and convergence results in the large sample regime are also provided. It is shown that the error probability decays exponentially fast as the number of samples in each data sequence goes to infinity. The error exponent has a simple form regardless of the distance metric applied when certain conditions are satisfied. In particular, the error exponent is characterized when either the Kolmogrov-Smirnov distance or the maximum mean discrepancy are used as the distance metric. Simulation results are provided to validate the analysis.

本文研究使用k-medoids算法对数据序列进行聚类。提出了基于分布距离度量的k-medoids算法，对已知和未知的分布簇数进行聚类分析。提供了错误概率的上限和大样本情况下的收敛结果。当符合某些条件时，而且使用Kolmogrov-Smirnov距离或最大平均差异作为距离度量时，错误指数具有简单的形式。模拟结果证明了所提出分析的可行性。

使用复合分布的K-medoids数据序列聚类