Visual Mining of Epidemic Networks

We show how an interactive graph visualization method based on maximal modularity clustering can be used to explore a large epidemic network. The visual representation is used to display statistical tests results that expose the relations between the propagation of HIV in a sexual contact network and the sexual orientation of the patients.Comment: 8 page

Facet-Based Browsing in Video Retrieval: A Simulation-Based Evaluation

In this paper we introduce a novel interactive video retrieval approach which uses sub-needs of an information need for querying and organising the search process. The underlying assumption of this approach is that the search effectiveness will be enhanced when employed for interactive video retrieval. We explore the performance bounds of a faceted system by using the simulated user evaluation methodology on TRECVID data sets and also on the logs of a prior user experiment with the system. We discuss the simulated evaluation strategies employed in our evaluation and the effect on the use of both textual and visual features. The facets are simulated by the use of clustering the video shots using textual and visual features. The experimental results of our study demonstrate that the faceted browser can potentially improve the search effectiveness

Visualization Support to Interactive Cluster Analysis

We demonstrate interactive visual embedding of partition-based clustering of multidimensional data using methods from the open-source machine learning library Weka. According to the visual analytics paradigm, knowledge is gradually built and refined by a human analyst through iterative application of clustering with different parameter settings and to different data subsets. To show clustering results to the analyst, cluster membership is typically represented by color coding. Our tools support the color consistency between different steps of the process. We shall demonstrate two-way clustering of spatial time series, in which clustering will be applied to places and to time steps

A cluster-based simulation of facet-based search

The recent increase of online video has challenged the research in the field of video information retrieval. Video search engines are becoming more and more interactive, helping the user to easily find what he or she is looking for. In this poster, we present a new approach of using an iterative clustering algorithm on text and visual features to simulate users creating new facets in a facet-based interface. Our experimental results prove the usefulness of such an approach

Simulated evaluation of faceted browsing based on feature selection

In this paper we explore the limitations of facet based browsing which uses sub-needs of an information need for querying and organising the search process in video retrieval. The underlying assumption of this approach is that the search effectiveness will be enhanced if such an approach is employed for interactive video retrieval using textual and visual features. We explore the performance bounds of a faceted system by carrying out a simulated user evaluation on TRECVid data sets, and also on the logs of a prior user experiment with the system. We first present a methodology to reduce the dimensionality of features by selecting the most important ones. Then, we discuss the simulated evaluation strategies employed in our evaluation and the effect on the use of both textual and visual features. Facets created by users are simulated by clustering video shots using textual and visual features. The experimental results of our study demonstrate that the faceted browser can potentially improve the search effectiveness

Integrating cluster formation and cluster evaluation in interactive visual analysis

Cluster analysis is a popular method for data investigation where data items are structured into groups called clusters. This analysis involves two sequential steps, namely cluster formation and cluster evaluation. In this paper, we propose the tight integration of cluster formation and cluster evaluation in interactive visual analysis in order to overcome the challenges that relate to the black-box nature of clustering algorithms. We present our conceptual framework in the form of an interactive visual environment. In this realization of our framework, we build upon general concepts such as cluster comparison, clustering tendency, cluster stability and cluster coherence. Additionally, we showcase our framework on the cluster analysis of mixed lipid bilayers

Analyzing eye movement patterns to improve map design

Recently, the use of eye tracking systems has been introduced in the field of cartography and GIS to support the evaluation of the quality of maps towards the user. The quantitative eye movement metrics are related to for example the duration or the number of the fixations which are subsequently (statistically) compared to detect significant differences in map designs or between different user groups. Hence, besides these standard eye movement metrics, other - more spatial - measurements and visual interpretations of the data are more suitable to investigate how users process, store and retrieve information from a (dynamic and/or) interactive map. This information is crucial to get insights in how users construct their cognitive map: e.g. is there a general search pattern on a map and which elements influence this search pattern, how do users orient a map, what is the influence of for example a pan operation. These insights are in turn crucial to be able to construct more effective maps towards the user, since the visualisation of the information on the map can be keyed to the user his cognitive processes. The study focuses on a qualitative and visual approach of the eye movement data resulting from a user study in which 14 participants were tested while working on 20 different dynamic and interactive demo-maps. Since maps are essentially spatial objects, the analysis of these eye movement data is directed towards the locations of the fixations, the visual representation of the scanpaths, clustering and aggregation of the scanpaths. The results from this study show interesting patterns in the search strategies of users on dynamic and interactive maps