标题： 从观测数据云和属性数据云的对偶空间中进行中等维度的大规模数据聚类 显示英文标题

标题： Massive Data Clustering in Moderate Dimensions from the Dual Spaces of Observation and Attribute Data Clouds

摘要： 高维数据的聚类分析可以受益于这种高维性的特性。 非正式地表达，在这项工作中，我们的重点是当维度相对于大量观测值来说是中等或较小时的类似情况。 数学上表达，这些是观测和属性的对偶空间。 观测点云位于属性空间中，属性点云位于观测空间中。 在本文中，我们首先总结与多变量分析中使用的方法论相关的各种观点。 我们利用这些观点建立一个高效的聚类处理流程，包括划分聚类和层次聚类。 显示英文摘要

摘要： Cluster analysis of very high dimensional data can benefit from the properties of such high dimensionality. Informally expressed, in this work, our focus is on the analogous situation when the dimensionality is moderate to small, relative to a massively sized set of observations. Mathematically expressed, these are the dual spaces of observations and attributes. The point cloud of observations is in attribute space, and the point cloud of attributes is in observation space. In this paper, we begin by summarizing various perspectives related to methodologies that are used in multivariate analytics. We draw on these to establish an efficient clustering processing pipeline, both partitioning and hierarchical clustering.