Traditional visualization techniques for multidimensional data sets, such as parallel coordinates, glyphs, and scatterplot matrices, do not scale well to high numbers of dimensions. A common approach to solving this problem is dimensionality reduction. Existing dimensionality reduction techniques usually generate lower dimensional spaces that have little intuitive meaning to users and allow little user interaction. In this paper we propose a new approach to handling high dimensional data, named Visual Hierarchical Dimension Reduction (VHDR), that addresses these drawbacks. VHDR not only generates lower dimensional spaces that are meaningful to users, but also allows user interactions in most steps of the process. In VHDR, dimensions are grouped into a hierarchy, and lower dimensional spaces are constructed using clusters of the hierarchy. We have implemented the VHDR approach into XmdvTool, and extended several traditional multidimensional visualization methods to convey dimension cluster characteristics when visualizing the data set in lower dimensional spaces. Our case study of applying VHDR to a real data set supports our belief that this approach is effective in supporting the exploration of high dimensional data sets.

“Words and rocks contain a language that follows a syntax of splits and ruptures. Look at any word long enough and you will see it open up into...a terrain of particles, each containing its own void... ” Robert Smithson (1996) This article presents both theoretical and technical bases on which to build a ‘‘science of text visualization.’’ These conceptually produce ‘‘the ecological approach,’’ which is rooted in ecological and evolutionary psychology. The basic idea is that humans are genetically selected from their species history to perceptually interpret certain informational aspects of natural environments. If information from text documents is visually spatialized in a manner conformal with these predilections, its meaningful interpretation to the user of a text visualization system becomes relatively intuitive and accurate. The SPIRE text visualization system, which images information from free text documents as natural terrains, serves as an example of the ‘‘ecological approach’’ in its visual metaphor, its text analysis, and its spatializing procedures. This article both formalizes Smithson’s evocative prose and responds to Steven Eick’s recent challenge (Eick, l997) to proceed to a real “science of information visualization.” It describes the theoretical rationale and technical basis of

We present a novel, innovative user interface to music repositories. Given an arbitrary collection of digital music files, our system creates a virtual landscape which allows the user to freely navigate in this collection. This is accomplished by automatically extracting features from the audio signal and training a Self-Organizing Map (SOM) on them to form clusters of similar sounding pieces of music. Subsequently, a Smoothed Data Histogram (SDH) is calculated on the SOM and interpreted as a three-dimensional height profile. This height profile is visualized as a three-dimensional island landscape containing the pieces of music. While moving through the terrain, the closest sounds with respect to the listener’s current position can be heard. This is realized by anisotropic auralization using a 5.1 surround sound model. Additionally, we incorporate knowledge extracted automatically from the web to enrich the landscape with semantic information. More precisely, we display words and related images that describe the heard music on the landscape to support the exploration.

To gain insight and understanding of complex information collections, users must be able to visualize and explore many facets of the information. This paper presents several novel visual methods from an information analyst's perspective. We present a sample scenario, using the various methods to gain a variety of insights from a large information collection. We conclude that no single paradigm or visual method is sufficient for many analytical tasks. Often a suite of integrated methods offers a better analytic environment in today's emerging culture of information overload and rapidly changing issues. We also conclude that the interactions among these visual paradigms are equally as important as, if not more important than, the paradigms themselves. Keywords: information visualization, user scenario, information analysis, document analysis 1 Introduction Information analysts face significant challenges dealing with the growing amount of information available and how to gain needed i...

Investigative analysts who work with collections of text documents connect embedded threads of evidence in order to formulate hypotheses about plans and activities of potential interest. As the number of documents and the corresponding number of concepts and entities within the documents grow larger, sense-making processes become more and more difficult for the analysts. We have developed a visual analytic system called Jigsaw that represents documents and their entities visually in order to help analysts examine reports more efficiently and develop theories about potential actions more quickly. Jigsaw provides multiple coordinated views of document entities with a special emphasis on visually illustrating connections between entities across the different documents.

A number of taxonomies to classify and categorize software visualization systems have been proposed in the past. Most notable are those presented by Price [1993] and Roman [1993]. While these taxonomies are an accurate representation of software visualization issues, they are somewhat skewed with respect to current research areas on software visualization. We revisit this important work and propose a number of realignments with respect to addressing the software engineering tasks of large-scale development and maintenance. We propose a framework to emphasize the general tasks of understanding and analysis during development and maintenance of large-scale software systems. Five dimensions relating to the what, where, how, who, and why of software visualization make up this framework. The focus of this work is not so much as to classify software visualization system, but to point out the need for matching the method with the task. Lastly, a number of software visualization systems are examined under our framework to highlight the particular problems each addresses.

An association rule in data mining is an implication of the form X Y where X is a set of antecedent items and Y is the consequent item. For years researchers have developed many tools to visualize association rules. However, few of these tools can handle more than dozens of rules, and none of them can effectively manage rules with multiple antecedents. Thus, it is extremely difficult to visualize and understand the association information of a large data set even when all the rules are available. This paper presents a novel visualization technique to tackle many of these problems. We apply the technology to a text mining study on large corpora. The results indicate that our design can easily handle hundreds of multiple antecedent association rules in a three-dimensional display with minimum human interaction, low occlusion percentage, and no screen swapping. Keywords: Text Visualization, Information Visualization, Text Mining, Data Mining, Association Rule 1 INTRODUCTION Association...

The term ‘proximity data ’ refers to data sets within which it is possible to assess the similarity of pairs of objects. Multidimensional scaling (MDS) is applied to such data and attempts to map high-dimensional objects onto low-dimensional space through the preservation of these similarity relationships. Standard MDS techniques have in the past suffered from high computational complexity and, as such, could not feasibly be applied to data sets over a few thousand objects in size. Through a novel hybrid approach based upon stochastic sampling, interpolation and spring models, we have designed an algorithm running in O(N÷N). Using Chalmers ’ 1996 O(N 2) spring model as a benchmark for the evaluation of our technique, we compare layout quality and run times using data sets of synthetic and real data. Our algorithm executes significantly faster than Chalmers ’ 1996 algorithm, whilst producing superior layouts. In reducing complexity and run time, we allow the visualisation of data sets of previously infeasible size. Our results indicate that our method is a solid foundation for interactive and visual exploration of data. 1.

data can be embedded within the space of the room or displayed on the walls. In this example, we have included the clustered document space from Figure 4 in the upper left-hand corner of the room. A map displayed on the wall references geographic-based data, and a representation of an analyst 's desk gives a link to information provided by a topic specialist. Other appropriate visualizations include a 5 Visual Web search for the term "animation." 6 Data vault based on the 3D room information space metaphor.

Techniques for improving the imageability of an existing data visualisation are described. The aim is to make the visualisation more easily explored, navigated and remembered. Starting from a relatively sparse landscape– like representation of a set of objects, we selectively add to the visualisation static features such as clusters, and dynamic features such as view–specific sampling of object detail. Information on past usage is used in this process, making manifest an aspect of interaction which is often neglected. Issues arising from the use of such features in a shared virtual environment are discussed.