Conference PaperPDF Available

Survey of Star Glyph-Based Visualization Technique for Multivariate Data

Authors:

Abstract and Figures

Background: The advancement of modern and scientific technology has led to dataset growing in both size and complexity, exposing the need for more efficient and effective way of visualizing and analyzing data. Current software architecture, management and analysis approaches are unable to cope with the flood of data Problem: Despites, the amount of progress in visualization methods especially multivariate data still poses a number of significant challenges in term of understand and depict with multivariate data involving more than two attribute dimensions. Objective: A technique using glyph-based visualization focusing in star glyph visualization is presented to visual and interpret multivariate data. Results:As a result, this paper investigates the general statistical of glyph-based application and details of star glyph-based applications based on interaction features. Conclusion: This paper reviews the existing guidelines, implementation techniques and surveying the use of star glyph-based visualization which uncommonly used for multivariate data visualization.
Content may be subject to copyright.
Australian Journal of Basic and Applied Sciences, 9(26) Special 2015, Pages: 61-66
ISSN:1991-8178
Australian Journal of Basic and Applied Sciences
Journal home page: www.ajbasweb.com
Corresponding Author: Izyan Izzati Kamsani, Universiti Teknologi Mara (Shah Alam), Department of Computer Science,
Faculty of Computer and Mathematical Science, 40000, Shah Alam, Selangor, Malaysia.
Ph: +6013-7493793 E-mail: izyanizzati@gmail.com
Survey of Star Glyph-Based Visualization Technique for Multivariate Data
Izyan Izzati Kamsani, Norharyati Md Ariff, Noor Elaiza Abd Khalid
Universiti Teknologi Mara (Shah Alam), Department of Computer Science, Faculty of Computer and Mathematical Science, 40000, Shah
Alam, Selangor, Malaysia.
A RT I CL E IN F O
A B ST R AC T
Article history:
Received 13 June 2015
Accepted 5 August 2015
Available online 12 August 2015
Keywords:
Data visualization, Glyph-based,
Multivariate data, Star glyph
Background: The advancement of modern and scientific technology has led to datasets
growing in both size and complexity, exposing the need for more efficient and effective
way of visualizing and analyzing data. Current software architecture, management and
analysis approaches are unable to cope with the flood of data. Problem: Despites, the
amount of progress in visualization methods especially multivariate data still poses a
number of significant challenges in term of understand and depict with multivariate
data involving more than two attribute dimensions. Objective: A technique using
glyph-based visualization focusing in star glyph visualization is presented to visual and
interpret multivariate data. Results: As a result, this paper investigates the general
statistical of glyph-based application and details of star glyph-based applications based
on interaction features. Conclusion: This paper reviews the existing guidelines,
implementation techniques and surveying the use of star glyph-based visualization
which uncommonly used for multivariate data visualization.
© 2015 AENSI Publisher All rights reserved.
To Cite This Article: Izyan Izzati Kamsani, Norharyati Md Ariff, Noor Elaiza Abd Khalid., Survey of Star Glyph-Based Visualization
Technique for Multivariate Data. Aust. J. Basic & Appl. Sci., 9(26): 61-66, 2015
INTRODUCTION
The analysis of growing datasets can be
extremely difficult to perform if the data is not
presented graphically as observed by Spence
(Spence, 2001) especially for multivariate data.
These data are hard to imagine where it is cannot be
easily observed by viewing large tables of data and
sometime face difficulty to visualize due to large
number of dimensions. One of techniques that are
commonly used to visualize multivariate data is
iconographic display (Matthew O Ward, 2002). This
technique is also known as glyph-based technique
(Dzemyda, Kurasova, & Zilinskas, 2013). Fanea et al
(Fanea, Carpendale, & Isenberg, 2005) pointed that
iconographic technique is able to map data in various
geometric and different colors for each attributes of
glyph. Visualizing pattern between multiple
dimensions data is one of the advantages using
glyph-based. It is also an effective method which
allows a quick comparison of data records and
attributes. These dimensions are known as
multivariate or multidimensional data. The definition
of multivariate is more broadly used to describe the
property of such datasets with high dimensionality.
While, for attribute less than three, the term
univariate and bivariate is frequently used to describe
datasets that contain only one or two dimensions
respectively. Visualizing multivariate data have been
widely used in various field such medical (Ropinski,
Oeltze, & Preim, 2011) (Kandogan, Road, & Jose,
2000), business (Marghescu, 2007) (Tekušová &
Knuth, 2008), engineering (Haber, 1990) and many
more (Fanea et al., 2005).
Glyph-based technique is divided into five main
branches such Chernoff faces, stick figure, shape
coding, color icon and star glyph. Star glyph-based
technique is some example of multivariate glyphs. It
suited for displaying multivariate and complex
datasets. Additionally, star glyph have been widely
used in the visualization of data and information and.
The objective of this survey is to deliver a better
understanding of the application of the star glyph-
based technique applied which applied in
multivariate data. This paper overviews the existing
guideline, implementation technique and applied
interaction technique. Discussion includes statistical
survey on glyph-based and star glyph publication and
analysis on features for star glyph technique.
In this paper, we reviewed star glyph-based
techniques to drive and facilitate of visualizing
multivariate data and some other interactive
technique applied in order to make this technique
become more interactive to user. Major gaps in the
literature, reviewing the existing guidelines and
implementation techniques are also been studied. The
62 Izyan Izzati Kamsani et al, 2015
Australian Journal of Basic and Applied Sciences, 9(26) Special 2015, Pages: 61-66
survey is organized as follows: Section 2 examines
the studies of star glyph background and number of
application areas where star glyph-based
visualization have been deployed. Section 3 surveys
for features, geometric channel and interactive
technique that have been used for practice. Section 4
summarizes the finding that has emerged during the
compilation of this survey.
Methodology:
The survey on glyph-based technique is
basically refer from Google Scholar, online journal
databases and from books related. These sources
were chosen where it is a platform for us to search
our literature, gaining plenty of information broadly
and find relevant work across the world in research
area. 30 papers between the periods 1990 to 2015
were reviewed. 20 papers related to glyph-based
technique in various applications were found.
However, there are 10 papers only which associated
to star glyph technique was reviewed. There were not
many papers discussed on star glyph technique since
this technique is rarely applied in multivariate data.
Glyph-based technique can be divided into five main
branches that are Chernoff faces, stick figure, shape
coding, color icon and star glyph.
Briefly explain, the most famous in iconography
(Chernoff, 1973) is Chernoff face visualization. This
technique work by mapping two attributes into the
2D position of a face. While the remaining attributes
are mapped to its properties, such the shape of nose,
mouth, eyes and the face itself. It also stated that
Chernoff faces can only visualize a limited amount
of data items (D. a. Keim & Kriegel, 1996). There is
common issue involve for multivariate icons where
the significant impact on the perceptive effectiveness
regarding on semantic relation (Spence, 2001).
Stick figure is another member of glyph-based
technique. This technique display two attributes and
the remaining attributes are mapped to the angles and
/ or limb length to the stick of figure icon (D. A.
Keim, 2002). If the data items are quite compact with
respect to the two dimensionally display, the
resulting visualization of texture patterns which
differ according to the characteristic of data and
determined by pre-attentive perception.
The other technique in glyph-based technique is
shape coding. This technique visualizes data using
small array of pixels. Pixels in the array are placed in
the form of square or rectangle. Meanwhile, the
arrays are arranged in a line-by-line pattern. In
details, each data items are represented by one such
arrays and the pixels are encoded with grayscale
according to the attributes values (Beddow, 1990). In
other hands, it is highly compressed visualization
without any clutter or overlap.
A combination of pixel-based spiral axes and
icon-based shape coding techniques perform color
icon technique (Levkowitz, 1991). Pixels are
replaced by array of color fields that represent the
attribute values similar to shape coding. Color,
shape, size, orientation, boundaries and area sub-
dividers can be used to map the multivariate data.
Star glyph is one of the most widely used glyphs
(Fanea et al., 2005) for multivariate data. It represent
the attributes as equal angular axes release from the
center of a circle (Chernoff, 1973) with an outer line
connecting the data value points on each axis. Each
data item or record is presented by a single star
glyph.
Glyph-based Table Review:
There are five (5) techniques that are categorized
under glyph-based technique. These techniques
contain Chernoff faces, stick figure, shape coding,
color icon and star glyph. Figure 1 summarized
specifically five (5) techniques in the glyph-based
techniques including references.
Glyphs represent the data value using visual
features or attributes such as shape, size, colour and
position. Normally, the number of graphical object is
equal to the cardinality of the high dimensional
dataset and they are arranged on the display in such
way as to reveal visual pattern. Since a large number
of data variations can be incorporated into properties
of a single glyph, this makes it a highly suitable
technique for communicating and supporting
multivariate analysis. Glyph can be placed and
viewed either independently from others. But in
some cases, glyph can be spatially connected to
convey the topological relationships between data
points of the underlying data specification. It is
expected that these pattern represent interesting
behaviour of the data. Glyphs are effective method to
visualize pattern between multiple dimensions and
allow a quick comparison of data records and
attributes.
As a summary, glyph-based technique can
handle from small to medium datasets with a few
thousand data items. But, glyph-based technique can
also been applied to dataset which are multivariate
data and these data attributes are treated differently
as some visual attributes of the icons may attract
more attention than the others. In other way, the way
data attributes are mapped to icon are greatly
determines the expressiveness of the resulting
visualization and what can perceived. There is a
difficulty regarding on glyph-based technique during
mapping the data when come to complex
multivariate data. Additionally, it requires training to
interpret the icons since it is not a straight forward
interpretation. There is also occurs an overlap
records if data attributes are mapped to the icon’s
display positions. As illustrated from Figure 1,
Chernof faces and star glyph are mostly discussed
and reviewed by other researchers. This is due to
unique characteristics between these two techniques.
However, this paper only focused on star glyph
technique.
63 Izyan Izzati Kamsani et al, 2015
Australian Journal of Basic and Applied Sciences, 9(26) Special 2015, Pages: 61-66
Fig. 1: Categorization of glyph-based technique.
Table 1 shows comparison of five (5) features of
the glyph-based techniques. A study regarding this
topic have been done on several papers (D. A. Keim,
2002),(Cristina, Oliveira, & Levkowitz,
2003),(Elmqvist & Fekete, n.d.),(Chan, 2006). We
categorized each features into specific category such
data, type of multi-dimensional data, visualization
specification, user’s interaction, finding and
limitation each techniques in glyph-based technique
Table 1: An analysis of features and requirement of star glyph based technique.
Data feature is a feature where it is classified
into representation and type. Chernoff face, stick
figure, shape coding, color icon and star glyph are
commonly used to visualize multidimensional or
multivariate data. Chernoff face respectively
visualize the data into face, stick figure into stick,
shape coding into rectangular grid, color icon in
color based and star glyph into star representation.
Categorical data can also be visualized using glyphs,
though only after conversion to numerical data form.
Interaction to users is important so that users are free
to play around with the data based on their
requirement. Brushing enable users to resize the
value of the dimension and this stage is crucial to
observe the relationship between the attributes in the
specific range of the scaling. Labeling is also
important and it is preferable to apply into star glyph.
Labeling can give details of data as supplementary
information to the user by clicking the star glyph
right away without making any crowded of data
view. RESULT AND DISCUSSION
The result of investigation is discussed in two
main section; the general statistical of glyph-based
Glyph-Based
Chernoff
Faces
(Spence,
2001),(Cher
noff,
1973),(D. a.
Keim &
Kriegel,
1996),(Sedl
mair et al.,
2013)
Shape Coding
(Beddow, 1990)
Color Icon
(Levkowitz,
1991)
Star Glyph
(Chernoff,
1973),(Sed
lmair,
Munzner,
& Tory,
2013),(M.
O. Ward,
1994)
64 Izyan Izzati Kamsani et al, 2015
Australian Journal of Basic and Applied Sciences, 9(26) Special 2015, Pages: 61-66
applications and the details of star glyph-based
techniques based on interaction features.
Population of Glyph-based Applications between
1990 to 2015:
Researchers that apply glyph-based technique in
visualizing multivariate data began around 1990s.
But there is an increasing amount of research
applying star glyph technique for multivariate data
starting from 2009. This technique is rarely used for
visualizing compared to Chernoff face which is quite
famous long time ago. However, star glyph technique
has become a popular choice of illustrating complex
data that involve multivariate data and applied in
various applications. Figure 2 depicted the number of
publication for glyph-based from 1990 until 2015.
Fig. 2: Glyph-based technique publication by year.
Application of Star Glyph-based Technique based
on Multivariate Data:
In this section, survey collections of applications
in various fields where star glyph-based visualization
has already made an impact. Generally, glyph-based
technique explains according to the data’s
characteristics which make them fundamentally
different. Thus, selecting the preferable technique
would be depends on the data which are visualized
and the task that need to be performed by the user
over the visualization (Rzeźniczak, 2013). There are
three types of data such event-based, geo-spatial and
high-dimensional data. Briefly explain, event-based
data is an event that occur at a given time or location.
A popular and classic approach that combines the
visualization of space and time is the space-time
cube concept developed by Hagerstrand in 1989
(Hagerstrand, 1989). While Forlines and Wittenburg
(Forlines & Wittenburg, 2010) introduced Wakame
glyph to depict multi-dimensional sensor reading.
Geo-spatial involved analyzing data with a
geographical or geo-spatial aspect. MacEachren
(MacEachren, Brewer, & Pickle, 1998) present a
novel approach to visualize reliability in mortality
maps using a bi-variate mapping. Lastly, high
dimensional data is a desirable tool due to their
applicability to a diverse range of fields. One of the
most popular used approaches is star glyph which
was first introduced by Siegal et al. Star glyph so
effective when applied in multivariate data where its
ability to compare similarities between multivariate
entities based on its geometric properties. It has used
to depict a variety of datasets which include
myocardial infarction data (Rzeźniczak, 2013), coal
data (M.O. Ward & Rundensteiner, 2004) and animal
datasets (Lee, Butavicius, & Reilly, 2003) as mention
in Figure 2.
Based on Figure 3 it shows that mostly glyph
have been applied in multivariate data. However,
research on star glyph specifically is still new for this
area which uncommonly used for multivariate data
visualization compared to glyph-based technique
generally. Figure 4 proves the statement according to
the research area between star glyph and glyph-based
technique. Thus, it is a constraint in order to do a
research in star glyph-based technique where the
sources are limited and we can summarize that this
technique is not widely applied compared to others.
Star Glyph-based Technique based on Interaction
Features:
We have implemented our visualization
technique using Microsoft Visual Studio (C#) to
generate the star glyph visualization. We used
benchmark data which is car datasets (Kandogan &
Jose, 2001) that contain 400 automobiles from the
1970’s until 1980’s. The attributes for this dataset
includes fuel efficiency (in miles per gallon-MPG),
acceleration (time from 0-60MPH in second), engine
displacement (in cubic inches), weight and
horsepower. We had improved our star glyph
visualization such adding legend for future reference
and differentiate each attributes applying different
colors as shown in Figure 5.
However, there is still need some enhancement
for better visualization and presentation. To make
65 Izyan Izzati Kamsani et al, 2015
Australian Journal of Basic and Applied Sciences, 9(26) Special 2015, Pages: 61-66
firm basic on star glyph, we had studied on nine (9)
papers between 2002 until 2015 which focused on
star glyph research area only. We categorized into
three (3) categories such features, geometric channels
(Chen & Floridi, 2013) and user interaction. We
believed that applying all these categories will
overcome any weaknesses and enhance star glyph
features in future.
Fig. 3: Different data characteristic used in star glyph applications.
Fig. 4: Comparison between glyph-based and star glyph according to visualization for multivariate data.
Fig. 5: Examples star glyph that are generated using our visualization technique (a) and image generated using
MATLAB (b).
Glyph-Based
Geo-spatial
(MacEachren et
al., 1998)
Event-based
(Hagerstrand,
1989),(Forlines
& Wittenburg,
2010)
High-
dimensional
(Rzeźniczak,
2013),(M.O.
Ward &
Rundensteiner,
2004),(Lee et al.,
2003)
chevrolet
chevelle malibu
buick skylark
320
plymouth
satellite
(a)
(b)
65 Izyan Izzati Kamsani et al, 2015
Australian Journal of Basic and Applied Sciences, 9(26) Special 2015, Pages: 61-66
A star glyph is a multivariate graphing technique
in which each attributes represent a ray or ‘spoke’.
Each ray performs an equal angular distance from an
origin which extends out via a connecting line. The
length of each line is proportional to the magnitude
of the variable compared to the maximum value of
all the variables. Star glyph visualization represent a
set of datasets each has one or more set of features.
Each star (records) represents a separate data record
and each branch of the star represent a different
attributes of datasets. Similar records share a
particular features will share the same corresponding
branch of the star. Additionally, star glyph based
technique allows manipulation of attributes
dimensions (Chan, 2006). An interactive view allows
users to view data in different angles, axis and
attributes manipulation (Nguyen, Nelmes, Huang,
Simoff, & Catchpoole, 2014). Applying colours are
also allow instant recognition of similarities or
differences of the large data items and expressed
attributes relationship (D. A. Keim, 2002).
For a detailed overview of research on data
glyph, we refer the interested paper for summary. We
have been studied the perception of glyph in the
context of similarity tasks between year 2000 until
2015 publication. In this section, we were inspired by
two main bodies of work: a) related studies
investigating the performance of star glyph in
information visualization for multivariate data and b)
number of application which implement this
technique.
Based on Table 3, features give dimensionality,
representation, data similarity, attributes, mapping,
and labelling, numerical, categorical, overlap and
closed versus open contour. Below are the analyses
during the study on multivariate data in star glyph
visualization:
Dimensionality star glyph was selected as the
one with the highest dimensionality where it is still
not limitless. Authors Dias M.M et al. (Dias,
Yamaguchi, Rabelo, & Franco, 2012) had mention in
his paper that more than 80 attributes may contribute
to blur visualization.
Attribute’s domain type star glyph is the
suitable technique for representing numerical value
based on the data type perspective. However it is not
suitable for nominal qualitative data but it can be
done by assign them into ‘0’ and ‘1’ numerical value
for example to ease during plotting time. Hence, it is
easy to notice that the visual representation is built of
symptoms with ‘0’ and ‘1’ which contribute to the
picture indirectly.
Scalability star glyph can present only limited
number of object simultaneously because of the
space occupied by the graphical entity. However,
according to the task type, the limited scalability of
star glyph is not an obstacle here.
Labelling labelling gives user the ability to
display any important information or either hide the
label if necessary. Each spokes may have its own
label or may have more details to display in the
visualization. For instance, user may be interested in
the exact of the spoke represents. By clicking in that
particular star glyph, detailed information about that
category will be displayed. Using this feature, the
user can view the specific details for focal point to
compare and analysis.
Interaction visualization looks at the ability to
navigate and interrogate datasets through interaction
to improve understanding. However, like in many
multivariate approaches, the interaction of star glyph-
based visualization is an important aspect for the
visual exploration of complex datasets. There are
also challenges for viewing data due to screen space,
resolution and displaying many glyphs which can
cause data overlaps. Besides, it is difficult to
interpret the relationship between these data in order
to make fast decision and summarization.
Conclusion:
We do not state this opinion are complete or
describe a theory, but still the observation we have
made during our literature reviewed. We believe it
can be a helpful survey for the interested reader.
Literature review based on star glyph is limited since
2012. In future will be more experiment in
visualizing multivariate and complex data using star
glyph visualization.
ACKNOWLEDGEMENT
Izyan Izzati Kamsani is a PhD student in
Computer Science. Norharyati Md Ariff is a master
student in Computer Science and Dr. Noor Elaiza A.
Khalid is a senior lecturer in Faculty of Computer and
Mathematical Sciences, Universiti Teknologi MARA
(UiTM) Shah Alam, Malaysia. Author’ email
addresses: izyanizzati@gmail.com
REFERENCES
Beddow, J., 1990. Shape coding of
multidimensional data on a microcomputer display.
Proceedings of the First IEEE Conference on
Visualization: Visualization `90.
http://doi.org/10.1109/VISUAL.1990.146387
Chan, W.W., 2006. A Survey on Multivariate
Data Visualization.
Chen, M., L. Floridi, 2013. An analysis of
information visualization. Syntheses, 190(Synthese),
34213438.
Chernoff, H. (1973). The Use of Faces to
Represent Points in K-Dimensional Space
Graphically. Journal of the American Statistical
Association, 68(342): 361-368.
Cristina, M., Oliveira, F. De, H. Levkowitz,
2003. From Visual Data Exploration to Visual Data
Mining : A Survey. IEEE TRANSACTIONS ON
VISUALIZATION AND COMPUTER GRAPHICS,
9(3): 378-394.
66 Izyan Izzati Kamsani et al, 2015
Australian Journal of Basic and Applied Sciences, 9(26) Special 2015, Pages: 61-66
Dias, M.M., J.K. Yamaguchi, E. Rabelo, C.
Franco, 2012. Visualization Techniques : Which is
the Most Appropriate in the Process of Knowledge
Discovery in Data Base ? Advances in Data Mining
Knowledge Discovery and Applications, 155-180.
http://doi.org/10.5772/3349.
Dzemyda, G., O. Kurasova, J. Zilinskas, 2013.
Multidimensional Data Visualization. (G. Dzemyda,
O. Kurasova, & J. Zilinskas, Eds.). Springer
Optimization and Its Applications.
Elmqvist, N., J.D. Fekete, (n.d.)., Hierarchical
aggregation for information visualization: overview,
techniques, and design guidelines. IEEE
Transactions on Visualization and Computer
Graphics, 16(3): 439-54.
http://doi.org/10.1109/TVCG.2009.84.
Fanea, E., S. Carpendale, T. Isenberg, 2005. An
interactive 3D integration of Parallel Coordinates and
Star Glyphs. Proceedings - IEEE Symposium on
Information Visualization, INFO VIS, 149-156.
http://doi.org/10.1109/INFVIS.2005.1532141.
Forlines, C., K. Wittenburg, 2010. Wakame:
Sense making of multi-dimensional spatial-temporal
data. Proceedings of the Workshop on Advanced
Visual Interfaces AVI, 33-40.
http://doi.org/10.1145/1842993.1843000.
Haber, R.B., 1990. Visualization techniques for
engineering mechanics. Computing Systems in
Engineering, 1(1): 37-50.
http://doi.org/10.1016/0956-0521(90)90046-N.
Hagerstrand, 1989. What About People in
Regional Science.Pdf. Ninth European Congress of
The Regional Science Association.
Kandogan, E., S. Jose, 2001. Visualizing Multi-
dimensional Clusters , Trends , and Outliers using
Star Coordinates. In Proceedings of the Seventh
ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining, 107-116.
Kandogan, E., H. Road, S. Jose, 2000. Star
Coordinates : A Multi-dimensional Visualization
Technique with Uniform Treatment of Dimensions.
Keim, D.A., 2002. Information Visualization
and Visual Data Mining. IEEE TRANSACTIONS ON
VISUALIZATION AND COMPUTER GRAPHICS,
8(1): 1-8.
Keim, D.A., H.P. Kriegel, 1996. Visualization
techniques for mining large databases: a comparison.
IEEE Transactions on Knowledge and Data
Engineering, 8(6): 923-938.
http://doi.org/10.1109/69.553159.
Lee, M.D., M.A. Butavicius, R.E. Reilly, 2003.
Visualizations of binary data: A comparative
evaluation. International Journal of Human
Computer Studies, 59(5): 569-602.
http://doi.org/10.1016/S1071-5819(03)00082-X.
Levkowitz, H., 1991. Color icons-merging color
and texture perception for integrated visualization of
multiple parameters. Proceeding Visualization ’91:
164-170.
http://doi.org/10.1109/VISUAL.1991.175795.
MacEachren, A.M., C.A. Brewer, L.W. Pickle,
1998. Visualizing georeferenced data: representing
reliability of health statistics. Environment and
Planning A, 30(9): 1547-1561.
http://doi.org/10.1068/a301547.
Marghescu, D., 2007. Multidimensional Data
Visualization Techniques for Financial Performance
Data: A Review. TUCS Technical Report, 810(July).
Retrieved from
http://users.abo.fi/dmarghes/publications_files/TR81
0.pdf.
Nguyen, Q. V., G. Nelmes, M.L. Huang, S.
Simoff, D. Catchpoole, 2014. Interactive
Visualization for Patient-to-Patient Comparison.
Genomics & Informatics, 12(1): 21-34.
http://doi.org/10.5808/GI.2014.12.1.21.
Pickett, R.M., G.G. Grinstein, 1988.
Iconographic Displays For Visualizing
Multidimensional Data. In Systems, Man, and
Cybernetics, 1988. Proceedings of the 1988 IEEE
International Conference, 1: 514-519.
Ropinski, T., S. Oeltze, B. Preim, 2011. Survey
of glyph-based visualization techniques for spatial
multivariate medical data. Computers and Graphics
(Pergamon), 35(2): 392-401.
http://doi.org/10.1016/j.cag.2011.01.011.
Rzeźniczak, T., 2013. Evaluation of
multidimensional visualization techniques for
medical patterns representation. Journal of
Theoretical and Applied Computer Science, 7(4): 70-
85. Sedlmair, M., T. Munzner, M. Tory, 2013.
Empirical guidance on scatterplot and dimension
reduction technique choices. IEEE Transactions on
Visualization and Computer Graphics, 19(12): 2634-
43. http://doi.org/10.1109/TVCG.2013.153.
Spence, R., 2001. Information Visualization.
ACM Press Book.
Tekušová, T., M. Knuth, 2008. Data quality
visualization for multivariate hierarchic data. InfoVis
Demo. Http://www. , 12. Retrieved from
http://bib.dbvis.de/uploadedFiles/infovis08-
paper.pdf.
Ward, M.O., 1994. XmdvTool: integrating
multiple methods for visualizing multivariate data.
Proceedings Visualization ’94, 7.
http://doi.org/10.1109/VISUAL.1994.346302.
Ward, M.O., 2002. A taxonomy of glyph
placement strategies for multidimensional data
visualization. Information Visualization, 1(3-4): 194-
210. http://doi.org/10.1057/palgrave.ivs.9500025.
Ward, M.O., E.A. Rundensteiner, 2004. Clutter
Reduction in Multi-Dimensional Data Visualization
Using Dimension Reordering. IEEE Symposium on
Information Visualization, 89-96.
http://doi.org/10.1109/INFVIS.2004.15.
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Visualizing multi-dimensional data has tremendous effects on science, engineering, and business decision-making. A new visualization technique called Star Coordinates is presented to support users in early stages of their visual thinking activities. Star Coordinates arranges coordinates on a circle sharing the same origin at the center. It uses simply points to represent data, treating each dimension uniformly at the cost of coarse representation. Current implementation of Star Coordinates provided valuable insight on several real data sets for cluster discovery and multi-factor analysis tasks. The work on Star Coordinates will continue on developing advanced transformations that will improve data understanding in multi-dimensions.
Article
Full-text available
Data visualization has the potential to assist humans in analysing and comprehending large volumes of data, and to detect patterns, clusters and outliers that are not obvious using non-graphical forms of presentation. For this reason, data visualizations have an important role to play in a diverse range of applied problems, including data exploration and mining, information retrieval, and intelligence analysis. Unfortunately, while various different approaches are available for data visualization, there have been few rigorous evaluations of their effectiveness. This paper presents the results of three controlled experiments comparing the ability of four different visualization approaches to help people answer meaningful questions for binary data sets. Two of these visualizations, Chernoff faces and star glyphs, represent objects using simple icon-like displays. The other two visualizations use a spatial arrangement of the objects, based on a model of human mental representation, where more similar objects are placed nearer each other. One of these spatial displays uses a common features model of similarity, while the other uses a distinctive features model. The first experiment finds that both glyph visualizations lead to slow, inaccurate answers being given with low confidence, while the faster and more confident answers for spatial visualizations are only accurate when the common features similarity model is used. The second experiment, which considers only the spatial visualizations, supports this finding, with the common features approach again producing more accurate answers. The third experiment measures human performance using the raw data in tabular form, and so allows the usefulness of visualizations in facilitating human performance to be assessed. This experiment confirms that people are faster, more confident and more accurate when an appropriate visualization of the data is made available.
Article
Full-text available
We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension.
Article
A novel method of representing multivariate data is presented. Each point in k-dimensional space, k≤18, is represented by a cartoon of a face whose features, such as length of nose and curvature of mouth, correspond to components of the point. Thus every multivariate observation is visualized as a computer-drawn face. This presentation makes it easy for the human mind to grasp many of the essential regularities and irregularities present in the data. Other graphical representations are described briefly.
Conference Paper
Visual clutter denotes a disordered collection of graphical entities in information visualization. Clutter can obscure the structure present in the data. Even in a small dataset, clutter can make it hard for the viewer to find patterns, relationships and structure. In this paper, we define visual clutter as any aspect of the visualization that interferes with the viewer's understanding of the data, and present the concept of clutter-based dimension reordering. Dimension order is an attribute that can significantly affect a visualization's expressiveness. By varying the dimension order in a display, it is possible to reduce clutter without reducing information content or modifying the data in any way. Clutter reduction is a display-dependent task. In this paper, we follow a three-step procedure for four different visualization techniques. For each display technique, first, we determine what constitutes clutter in terms of display properties; then we design a metric to measure visual clutter in this display; finally we search for an order that minimizes the clutter in a display
Conference Paper
A technique that harnesses color and texture perception to create integrated displays of 2D image-like multiparameter distributions is presented. The power of the technique is demonstrated by an example of a synthesized dataset and compared with several other proposed techniques. The nature of studies that are required to measure objectively and accurately the effectiveness of such displays is discussed
Conference Paper
The author presents a simple and flexible method of sharp coding for higher dimensional data sets that allows the database operator or the scientist quick access to promising patterns within and among records or samples. The example used is a 13-parameter set of solar wind, magnetosphere, and ground observation data collected hourly for 21 days in 1976. The software system is a prototype developed to demonstrate the glyph approach to depicting higher-dimensional data sets. The experiment was to depict all parameters simultaneously, to see if any global or local patterns emerged. This experiment proves that much more complex data can be presented for visual pattern extraction than standard methods allow