Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on clusters of time series data, we test our approach on a large real-world data set of time-oriented scientific research data, demonstrating how specific interesting views are automatically identified, supporting the analyst discovering interesting and visually understandable relationships.可视化聚类分析(visual cluster analysis)为分析师提供了极具价值的工具，使其能够基于代表性聚类簇及其关联关系，理解大规模数据集的内在结构。通常，所发现的聚类簇需要结合数据元素所附带的分类、数值或文本元数据(metadata)的上下文来解读。尽管这类元数据通常并不纳入聚类流程，但它们发挥着关键作用，在交互式聚类探索过程中必须予以考量。传统上，联动视图(linked-views)能够将聚类簇与元数据或底层聚类数据的其他属性相关联（或宽泛地说，建立关联关系）。采用联动视图的方式手动检视每个聚类簇的元数据分布，工作繁琐且极易产生搜索瓶颈，在大规模数据集场景下尤为如此。若要通过全交互式搜索来发掘潜在有用或有价值的聚类簇与元数据间关联关系，过程往往冗长繁琐。为解决这一问题，本文提出一种全新方法，用于辅助用户发现聚类簇与关联元数据间的有价值关联关系。该方法的目标是帮助分析师遍历潜在规模庞大的搜索空间。本文的研究聚焦于分类类型元数据，这类元数据可通过直方图(histogram)形式对单个聚类簇进行汇总展示。我们从给定的可视化聚类表征出发，计算基于聚类簇元数据类别分布的若干有趣度度量指标。利用这些度量指标，可自动针对聚类簇在分类元数据分布上的潜在价值进行评分与排序。系统会将已识别出的有价值关联关系在可视化聚类表征中高亮显示，方便用户快速检视。本文还介绍了一套可扩展的分类元数据有趣度评分系统，该系统覆盖了丰富的评分维度，同时也可拓展至数值元数据场景。系统还提供了适配的可视化表征形式，用于展示可视化关联关系以及计算得到的排序评分。本文以时间序列数据(time series data)的聚类簇为研究对象，在面向时序的大规模真实科研数据集上对所提方法进行了测试，验证了该方法能够自动识别特定的有价值视图，辅助分析师发掘具有可视化可读性的高价值关联关系。