We propose a method for the semi-automated refinement of the results of feature subset selection algorithms. Feature subset selection is a preliminary step in data analysis which identifies the most useful subset of features (columns) in a data table. So-called filter techniques use statistical ranking measures for the correlation of features. Usually a measure is applied to all entities (rows) of a data table. However, the differing contributions of subsets of data entities are masked by statistical aggregation. Feature and entity subset selection are, thus, highly interdependent. Due to the difficulty in visualizing a high-dimensional data table, most feature subset selection algorithms are applied as a black box at the outset of an analysis. Our visualization technique, SmartStripes, allows users to step into the feature subset selection process. It enables the investigation of dependencies and interdependencies between different feature and entity subsets. A user may even choose to control the iterations manually, taking into account the ranking measures, the contributions of different entity subsets, as well as the semantics of the features
Today’s politicians are confronted with new information technologies to\ud tackle complex decision-making problems. In order to make sustainable decisions,\ud a profound analysis of societal problems and possible solutions (policy options)\ud needs to be performed. In this policy-analysis process, different stakeholders are\ud involved. Besides internal direct advisors of the policy makers (policy analysts),\ud external experts from different scientific disciplines can support evidence-based decision making. Despite the alleged importance of scientific advice in the policy-making\ud process, it is observed that scientific results are often not used. In this work, a concept\ud is described that supports the collaboration between scientists and politicians. We propose a science–policy interface that is realized by including information visualization in the policy-analysis process. Therefore, we identify synergy effects between\ud both fields and introduce a methodology for addressing the current challenges of\ud science–policy interfaces with visualization. Finally, we describe three exemplary\ud case studies carried out in European research projects that instantiate the concept of\ud this approach
To this day, data-driven science is a widely accepted concept in the digital library (DL) context (Hey et al. in The fourth paradigm: data-intensive scientific discovery. Microsoft Research, 2009). In the same way, domain knowledge from information visualization, visual analytics, and exploratory search has found its way into the DL workflow. This trend is expected to continue, considering future DL challenges such as content-based access to new document types, visual search, and exploration for information landscapes, or big data in general. To cope with these challenges,
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, especially 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 interestingPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright 20XX ACM X-XXXXX-XX-X/XX/XX ...$10.00. ness 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.
This article looks at the current and future roles of information visualization, semantics visualization, and visual analytics in policy modeling. Many experts believe that you can't overestimate visualization's role in this respect.
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