Michael Hund

Visual analytics for concept exploration in subspaces of patient groups

Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.

Analysis of Patient Groups and Immunization Results Based on Subspace Clustering

Biomedical experts are increasingly confronted with what is often called Big Data, an important subclass of high-dimensional data. High-dimensional data analysis can be helpful in finding relationships between records and dimensions. However, due to data complexity, experts are decreasingly capable of dealing with increasingly complex data. Mapping higher dimensional data to a smaller number of relevant dimensions is a big challenge due to the curse of dimensionality. Irrelevant, redundant, and conflicting dimensions affect the effectiveness and efficiency of analysis. Furthermore, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We show the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we analyze relationships between patients, sets of patient attributes, and outcomes of a vaccination treatment by means of a subspace clustering approach. We present an analysis workflow and discuss future directions for high-dimensional (medical) data analysis and visual exploration.

The World's Languages Explorer: Visual Analysis of Language Features in Genealogical and Areal Contexts

This paper presents a novel Visual Analytics approach that helps linguistic researchers to explore the worlds languages with respect to several important tasks: (1) The comparison of manually and automatically extracted language features across languages and within the context of language genealogy, (2) the exploration of interrelations among several of such features as well as their homogeneity and heterogeneity within subtrees of the language genealogy, and (3) the exploration of genealogical and areal influences on the features. We introduce the Worlds Languages Explorer, which provides the required functionalities in one single Visual Analytics environment. Contributions are made for different parts of the system: We introduce an extended Sunburst visualization whose so‐called feature‐rings allow for a cross‐comparison of a large number of features at once, within the hierarchical context of the language genealogy. We suggest a mapping of homogeneity measures to all levels of the hierarchy. In addition, we suggest an integration of information from the areal data space into the hierarchical data space. With our approach we bring Visual Analytics research to a new application field, namely Historical Comparative Linguistics, and Linguistic and Areal Typology. Finally, we provide evidence of the good performance of our system in this area through two application case studies conducted by domain experts.

Magnostics: Image-Based Search of Interesting Matrix Views for Guided Network Exploration

In this work we address the problem of retrieving potentially interesting matrix views to support the exploration of networks. We introduce Matrix Diagnostics (or Magnostics), following in spirit related approaches for rating and ranking other visualization techniques, such as Scagnostics for scatter plots. Our approach ranks matrix views according to the appearance of specific visual patterns, such as blocks and lines, indicating the existence of topological motifs in the data, such as clusters, bi-graphs, or central nodes. Magnostics can be used to analyze, query, or search for visually similar matrices in large collections, or to assess the quality of matrix reordering algorithms. While many feature descriptors for image analyzes exist, there is no evidence how they perform for detecting patterns in matrices. In order to make an informed choice of feature descriptors for matrix diagnostics, we evaluate 30 feature descriptors-27 existing ones and three new descriptors that we designed specifically for MAGNOSTICS-with respect to four criteria: pattern response, pattern variability, pattern sensibility, and pattern discrimination. We conclude with an informed set of six descriptors as most appropriate for Magnostics and demonstrate their application in two scenarios; exploring a large collection of matrices and analyzing temporal networks.

Subspace Nearest Neighbor Search - Problem Statement, Approaches, and Discussion

Computing the similarity between objects is a central task for many applications in the field of information retrieval and data mining. For finding k-nearest neighbors, typically a ranking is computed based on a predetermined set of data dimensions and a distance function, constant over all possible queries. However, many high-dimensional feature spaces contain a large number of dimensions, many of which may contain noise, irrelevant, redundant, or contradicting information. More specifically, the relevance of dimensions may depend on the query object itself, and in general, different dimension sets subspaces may be appropriate for a query. Approaches for feature selection or -weighting typically provide a global subspace selection, which may not be suitable for all possibly queries. In this position paper, we frame a new research problem, called subspace nearest neighbor search, aiming at multiple query-dependent subspaces for nearest neighbor search. We describe relevant problem characteristics, relate to existing approaches, and outline potential research directions.

Spherical Similarity Explorer for Comparative Case Analysis

Comparative Case Analysis (CCA) is an important tool for criminal investigation and crime theory extraction. It analyzes the commonalities and differences between a collection of crime reports in order to understand crime patterns and identify abnormal cases. A big challenge of CCA is the data processing and exploration. Traditional manual approach can no longer cope with the increasing volume and complexity of the data. In this paper we introduce a novel visual analytics system, Spherical Similarity Explorer (SSE) that automates the data processing process and provides interactive visualizations to support the data exploration. We illustrate the use of the system with uses cases that involve real world application data and evaluate the system with criminal intelligence analysts.