PNNL-13501 - Pacific Northwest National Laboratory

Figure 1. A visualization technique to explore sequential
patterns
Figure 2 presents a novel technique to visualize many-toone
association rules. The rows of the matrix floor
represent the topics, and the columns represent the item
associations. The blue and red blocks of each column
(rule) represent the antecedent and the consequent of the
rule. The identities of the items are shown along the right
side of the matrix. The confidence and support levels of
the rules are given by the corresponding bar charts in
different scales at the far end of the matrix. The system
supports basic query commands through the use of pop-up
menus to restrict key items to be included in the
visualization.
In Figure 3, each data record of a multivariate data set is
represented as a series of line segments that together
create what we refer to as a line plot. Each line segment
reflects two measures of a characteristic of that data
record. Line plots are placed next to each other in the
visualization such that when no variation occurs in their
line segments, they are parallel with one another. When
used to represent a multivariate data set, the line plots
visually represent the degree to which the derived
measures for each parameter vary.
Figure 2. A visualization technique to explore association
rules
Figure 3. A visualization technique to explore anomalous
exceptions of a large multivariate dataset
Summary and Conclusions
This project presents data mining and visualization
techniques for discovery of patterns and exceptions from
large corpora. The strengths of the two approaches (data
mining and visualization) can compensate for each other’s
weakness. We subsequently introduced a powerful visual
data-mining environment that contains multiple datamining
engines to discover different patterns and
exceptions, and multiple visualization front-ends to show
the distribution and locality of the mining results. Our
experimental results show that we can quickly learn more
in such an integrated visual data-mining environment.
Publications and Presentations
Wong PC, W Cowley, H Foote, E Jurrus, and J Thomas.
2000. “Visualizing sequential patterns for text mining.”
Presented and In Proceedings IEEE Information
Visualization 2000, October 2000, IEEE Computer
Society Press, Los Alamitos, California.
Rose S and PC Wong. 2000. “DriftWeed - A visual
metaphor for interactive analysis of multivariate data.”
Proceedings IS&T/SPIE Conference on Visual Data
Exploration and Analysis, January 24 - 26, 2000, San
Jose, California.
Wong PC, P Whitney, and J Thomas. 1999. “Visualizing
association rules for text mining.” Presented and In
Proceedings IEEE Information Visualization 99, October
1999, IEEE Computer Society Press, Los Alamitos,
California.
Wong PC. 1999. “Visual data mining.” IEEE Computer
Graphics and Applications, 19(5).
Computer Science and Information Technology 163