Abstract not found

A multiple view system uses two or more distinct views to support the investigation of a single conceptual entity. Many such systems exist, ranging from computer-aided design (CAD) systems for chip design that display both the logical structure and the actual geometry of the integrated circuit to overview-plus-detail systems that show both an overview for context and a zoomed-in-view for detail. Designers of these systems must make a variety of design decisions, ranging from determining layout to constructing sophisticated coordination mechanisms. Surprisingly, little work has been done to characterize these systems or to express guidelines for their design. Based on a workshop discussion of multiple views, and based on our own design and implementation experience with these systems, we present eight guidelines for the design of multiple view systems. Keywords Multiple views, information visualization, design guidelines, usability heuristics, user interfaces INTRODUCTION Multiple v...

AbstractÐThe ThemeRiver visualization depicts thematic variations over time within a large collection of documents. The thematic changes are shown in the context of a time line andcorresponding external events. The focus on temporal thematic change within a context framework allows a user to discern patterns that suggest relationships or trends. For example, the sudden change of thematic strength following an external event may indicate a causal relationship. Such patterns are not readily accessible in other visualizations of the data. We use a river metaphor to convey several key notions. The document collection's time line, selected thematic content, and thematic strength are indicated by the river's directed flow, composition, and changing width, respectively. The directed flow from left to right is interpreted as movement through time and the horizontal distance between two points on the river defines a time interval. At any point in time, the vertical distance, or width, of the river indicates the collective strength of the selected themes. Colored ªcurrentsº flowing within the river represent individual themes. A current's vertical width narrows or broadens to indicate decreases or increases in the strength of the individual theme. Index TermsÐVisualization, metaphor, trendanalysis, time line. 1

Abstract ⎯ This paper describes the development and application of visualisation techniques for users to access and explore information in a digital library effectively and intuitively. Salient semantic structures and citation patterns are extracted from several collections of documents, including the ACM SIGCHI conference proceedings (1995 ⎯ 1997) and ACM Hypertext conference proceedings (1987 ⎯ 1998), using Latent Semantic Indexing and Pathfinder Network Scaling. The unique spatial metaphor leads to a natural combination of search and navigation within the same semantic space in a 3-dimensional virtual world. Author co-citation patterns are visualised through a number of author co-citation maps in attempts to reveal the structure of the field of hypertext, including an overall co-citation map of 367 authors and three periodical maps. These maps highlight predominant research areas in the field. This approach provides a means of transcending the boundaries of collections of documents and visualising more profound patterns in terms of semantic structures and co-citation networks. © 1999 Elsevier Science Ltd. All rights reserved. 1.

ThemeRiver ™ is a prototype system that visualizes thematic variations over time within a large collection of documents. The “river ” flows from left to right through time, changing width to depict changes in thematic strength of temporally associated documents. Colored “currents ” flowing within the river narrow or widen to indicate decreases or increases in the strength of an individual topic or a group of topics in the associated documents. The river is shown within the context of a timeline and a corresponding textual presentation of external events.

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.

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...

AB AB AB Org IN AF AD Source JN SO SO Year parse from PB PY DP Type DT PT PT Title TI TI TI Author AU AU AU Terms DE DE MH Table 3. Number of articles kept from each data source in combined data set.

This paper introduces a graphical method for visually presenting and exploring the results of multiple queries simultaneously. This method allows a user to visually compare multiple query result sets, explore various combinations among the query result sets, and identify the “best ” matches for combinations of multiple independent queries. This approach might also help users explore methods for progressively improving queries by visually comparing the improvement in result sets. 1.

A sequential pattern in data mining is a finite series of elements such as A B C D where A, B, C, and D are elements of the same domain. The mining of sequential patterns is designed to find patterns of discrete events that frequently happen in the same arrangement along a timeline. Like association and clustering, the mining of sequential patterns is among the most popular knowledge discovery techniques that apply statistical measures to extract useful information from large datasets. As our computers become more powerful, we are able to mine bigger datasets and obtain hundreds of thousands of sequential patterns in full detail. With this vast amount of data, we argue that neither data mining nor visualization by itself can manage the information and reflect the knowledge effectively. Subsequently, we apply visualization to augment data mining in a study of sequential patterns in large text corpora. The result shows that we can learn more and more quickly in an integrated visual...