An important work of data analysis is to identify correlation structures and classify the data in unlabeled high-dimensional data, which usually requires iterative experiments on clustering parameters, attribute weights and instances. For a large dataset, the number of clusters may be huge, and it is a great challenge to explore in this huge space. People usually have a more comprehensive understanding of some data. For example, they think that data A is better than data B, but they do not know which attributes are important. Therefore, a powerful interactive analysis tool can help people greatly improve the effectiveness of exploratory clustering analysis. This paper provides a visual analysis method for sorting and classifying multivariate data. It can determine the weight of each attribute through user’s interaction, thus, generating sorting, and then complete classification according to sorting results. Through visual display, users can understand the characteristics of data as well as category characteristics intuitively and quickly, and it helps users improve sorting and classification results.

中文翻译：

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多维数据排序与分类的可视化分析

数据分析的一项重要工作是识别相关结构并对未标记的高维数据中的数据进行分类，这通常需要对聚类参数、属性权重和实例进行迭代实验。对于一个大型数据集，集群的数量可能是巨大的，在这个巨大的空间中探索是一个很大的挑战。人们通常对一些数据有更全面的了解。例如，他们认为数据 A 比数据 B 好，但他们不知道哪些属性是重要的。因此，一个强大的交互式分析工具可以帮助人们大大提高探索性聚类分析的有效性。本文提供了一种对多元数据进行排序和分类的可视化分析方法。它可以通过用户的交互来确定每个属性的权重，从而生成排序，然后根据排序结果完成分类。通过可视化展示，用户可以直观、快速地了解数据的特征和类别特征，帮助用户提高排序和分类结果。