Interactive Visual Exploration Research Articles

Interactive visual exploration of association rules with rule-focusing methodology

On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge post-processing is a difficult stage in an association rule discovery process. In order to find...

Value and relation display: interactive visual exploration of large data sets with hundreds of dimensions.

Few existing visualization systems can handle large data sets with hundreds of dimensions, since high-dimensional data sets cause clutter on the display and large response time in interactive exploration. In this paper, we present a significantly improved multidimensional visualization approach named Value and Relation (VaR) display that allows users to effectively and efficiently explore large data sets with several hundred dimensions. In the VaR display, data values and dimension relationships are explicitly visualized in the same display by using dimension glyphs to explicitly represent values in dimensions and glyph layout to explicitly convey dimension relationships. In particular, pixel-oriented techniques and density-based scatterplots are used to create dimension glyphs to convey values. Multidimensional scaling, Jigsaw map hierarchy visualization techniques, and an animation metaphor named Rainfall are used to convey relationships among dimensions. A rich set of interaction tools has been provided to allow users to interactively detect patterns of interest in the VaR display. A prototype of the VaR display has been fully implemented. The case studies presented in this paper show how the prototype supports interactive exploration of data sets of several hundred dimensions. A user study evaluating the prototype is also reported in this paper.

Interactive Visual Analysis of Families of Function Graphs

The analysis and exploration of multidimensional and multivariate data is still one of the most challenging areas in the field of visualization. In this paper, we describe an approach to visual analysis of an especially challenging set of problems that exhibit a complex internal data structure. We describe the interactive visual exploration and analysis of data that includes several (usually large) families of function graphs fi (x, t). We describe analysis procedures and practical aspects of the interactive visual analysis specific to this type of data (with emphasis on the function graph characteristic of the data). We adopted the well-proven approach of multiple, linked views with advanced interactive brushing to assess the data. Standard views such as histograms, scatterplots, and parallel coordinates are used to jointly visualize data. We support iterative visual analysis by providing means to create complex, composite brushes that span multiple views and that are constructed using different combination schemes. We demonstrate that engineering applications represent a challenging but very applicable area for visual analytics. As a case study, we describe the optimization of a fuel injection system in diesel engines of passenger cars.

Interactive visual exploration of association rules with rule-focusing methodology

On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge post-processing is a difficult stage in an association rule discovery process. In order to find relevant knowledge for decision making, the user (a decision maker specialized in the data studied) needs to rummage through the rules. To assist him/her in this task, we here propose the rule-focusing methodology, an interactive methodology for the visual post-processing of association rules. It allows the user to explore large sets of rules freely by focusing his/her attention on limited subsets. This new approach relies on rule interestingness measures, on a visual representation, and on interactive navigation among the rules. We have implemented the rule-focusing methodology in a prototype system called ARVis. It exploits the user's focus to guide the generation of the rules by means of a specific constraint-based rule-mining algorithm.

Gesture-Tracking in Real Time with Dynamic Regional Range Computation

This paper presents a new approach to the range data utilization in a gesture-tracking system. The use of three-dimensional data is essential for human motion analysis; however, the speed of complete range estimation prohibits from including it in most real-time systems. This work describes a gesture-tracking system using real-time local range on-demand. The system represents a gesture-controlled interface for interactive visual exploration of large data sets. The paper describes a method performing range processing only when necessary and where necessary. Range data is processed only for non-static regions of interest. This is accomplished by a set of filters on the color, motion, and range data. The speed-up achieved is between 1.70 and 2.15. The algorithm also includes a robust skin-color segmentation insensitive to illumination changes. Selective range processing results in dynamic regional range images that contain only information needed by the system.

Mapping, modeling, and visual exploration of structure-function relationships in the heart

It is becoming clear that the emergent, integrative behaviors of biological systems result from complex interactions between all system components, and that knowledge of each component is not sufficient to understand such behaviors. In this paper, we describe our approach to the integrative modeling of cardiac function. This approach spans multiple levels of biological analysis, ranging from subcellular to tissue. We have applied diverse analytical methods, including imaging techniques for measurement of anatomic structure and biophysical and biochemical responses of cells and tissue, parallel computing techniques for the numerical solution of large systems of model equations, and interactive visual exploration of model dynamic behavior.

Interactive text retrieval based on document similarities

In this article we present a prototypical implementation of a software tool for document retrieval which groups/arranges (pre-processed) documents based on a similarity measure. The prototype was developed based on self-organising maps to realise interactive associative search and visual exploration of document databases. This helps a user to navigate through similar documents. The navigation, especially the search for the first appropriate document, is supported by conventional keyword search methods. The usability of the presented approach is shown by a sample search.