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.

(Table 2) Similarity Indexes (Pearson's correlation coefficient) of the silicoflagellate assemblages of IODP Exp302

The silicoflagellate and ebridian assemblages in early middle Eocene Arctic cores obtained by IODP Expedition 302 (ACEX) were studied in order to decipher the paleoceanography of the upper water column. The assemblages in Lithologic Unit 2 (49.7-45.1 Ma), one of the biosiliceous intervals, were usually endemic as compared to the assemblages that occurred outside of the Arctic Ocean. The presence of these endemic assemblages is probably due to a unique environmental setting, controlled by the degree of mixing between the low-salinity Arctic waters and relatively high salinity waters supplied from outside the Arctic Ocean, such as the Atlantic and possibly the Western Siberian Sea. Using the basin-to-basin fractionation model, the early middle Eocene Arctic Ocean corresponds to an estuarine circulation type, which includes the modern-day Black Sea. The abundant down-core occurrence of ebridians strongly suggests the past presence of low-salinity waters, and may indicate that low oxygen concentrations prevailed in the euphotic layer, on the basis of the ecology of the modern ebridian Hermesinum adriaticum.