A new clustering accuracy measure is proposed to determine the unknown number of clusters and to assess the quality of clustering of a data set given in any dimensional space. Our validity index applies the classical nonparametric univariate kernel density estimation method to the interpoint distances computed between the members of data. Being based on interpoint distances, it is free of the curse of dimensionality and therefore efficiently computable for high-dimensional situations where the number of study variables can be larger than the sample size. The proposed measure is compatible with any clustering algorithm and with every kind of data set where the interpoint distance measure can be defined to have a density function. Simulation study proves its superiority over widely used cluster validity indices like the average silhouette width and the Dunn index, whereas its applicability is shown with respect to a high-dimensional Biostatistical study of Alon data set and a large Astrostatistical application of time series with light curves of new variable stars.
A tree-based kernel for densities and its applications in clustering DNase-seq profiles
Department of Statistics & Data Science Institute, University of Chicago（芝加哥大学统计与数据科学研究所）；Department of Human Genetics, University of Chicago（芝加哥大学人类遗传学系）
Multiple kernel learning for integrative consensus clustering of 'omic datasets
MRC Biostatistics Unit（MRC生物统计学单元）；Cambridge Institute of Therapeutic Immunology & Infectious Disease（剑桥治疗免疫学与传染病研究所）；University of Cambridge（剑桥大学）
不同的应用程序-特别是在肿瘤亚型-已经证明了整合聚类技术的重要性，从多个数据源的信息相结合。聚类分析（COCA）就是一种广泛应用于肿瘤亚型分析的方法。然而，COCA的属性从未被系统地探索过，并且它对包含噪声数据集或定义冲突聚类结构的数据集的鲁棒性尚不清楚。我们严格基准COCA，并提出核学习集成聚类（KLIC）作为一种替代策略。KLIC将组合聚类结构的挑战框定为多核学习问题，其中不同的数据集各自为最终聚类提供加权贡献。这允许噪声数据集的贡献相对于信息量更大的数据集被降低权重。我们通过仿真研究比较了KLIC和COCA在各种情况下的性能。我们还介绍了KLIC和COCA在癌症亚型和转录模块发现的实际数据应用中的输出。R软件包“klic”和“coca”可以在Comprehensive R Archive Network上找到。
Statistical development and assessment of summary measures to account for isotopic clustering of Fourier transform mass spectrometry data in clinical diagnostic studies
