GUIDELINES FOR THE CHOICE OF VISUALIZATION
TECHNIQUES APPLIED IN THE PROCESS OF KNOWLEDGE
EXTRACTION
Juliana Keiko Yamaguchi, Maria Madalena Dias and Clélia Franco
Department of Informatic, State University of Maringá, Av. Colombo - 5.790, Maringá, Brazil
Keywords: Data visualization, Visualization techniques, Knowledge discovery, Data visualization parameters.
Abstract: Visualization techniques are tools that can improve analyst's insight into the results of knowledge discovery
process or to directly explore and analyze data. They allows analysts to interact with the graphical
representation to get new knowledge. The choice of visualization techniques must follow some criteria to
guarantee a consistent data representation. This paper presents a study based on Grounded Theory that
indicates parameters for select visualization techniques, which are: data type, task type, data volume, data
dimension and position of the attributes in the display. These parameters are analyzed in the context of
visualization technique categories: standard 1D - 3D graphics, iconographic techniques, geometric
techniques, pixel-oriented techniques and graph-based or hierarchical techniques. The analysis over the
association among these parameters and visualization techniques culminated in guidelines establishment to
choose the most appropriate techniques according to the data characteristics and the objective of the
knowledge discovery process.
1 INTRODUCTION
Represent the gained information in a visual way is a
solution for facilitate the understanding of analized
data. Thus, visualization techniques can be
integrated into the process of KDD, so much to
preview data to be analyzed or help in understanding
the results of data mining, so much to understand the
partial results of the iterations in the process of
extracting knowledge (Ankerst, 2001).
However, exploration and analysis of data using
visualization techniques directly applied to data can
bring useful and new knowledge, enough to exempt
other data mining techniques. Furthermore,
visualization is a powerful tool for conveying ideas,
due to vision plays an important role in human
cognition (Nascimento and Ferreira, 2005).
When visualization techniques are chosen to data
analysis, some criteria must be considered so that the
graphical representation really helps in
understanding data. First of all, it should be
observed relevant characteristics of the data, such as
data type, dimensionality (number of attributes) and
volume.
Tasks that users can perform during data
exploration may also be another factor in this
decision. Basically, the following tasks are the most
common mentioned in the literature: data overview,
verication of correlation among attributes,
identication of new rules or patterns, cluster
analysis and outliers detection. Furthermore,
depending on the visualization technique used,
positioning of the attributes in the graph can be
signicant in interpreting the behavior of data.
This paper presents a study whose research
methodology was based on Grounded Theory to
establish guidelines for choosing most suitable
visualization techniques according to the
characteristics of analyzed data.
So, in next section Grounded Theory
methodology is briey presented. Following, the
items: data type, dimensionality, volume, task type
and positioning of attributes in the graphic are the
named parameters identied through this
methodology and they are described in sequence.
Next, an analysis on the association of each
parameter to different types of visualization
techniques is discussed. After this, general
guidelines for choosing visualization techniques
according to the identied parameters are outlined.
183
Keiko Yamaguchi J., Madalena Dias M. and Franco C..
GUIDELINES FOR THE CHOICE OF VISUALIZATION TECHNIQUES APPLIED IN THE PROCESS OF KNOWLEDGE EXTRACTION.
DOI: 10.5220/0003469901830189
In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 183-189
ISBN: 978-989-8425-53-9
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
Finally, last section presents the conclusion of this
work.
2 RESEARCH METHODOLOGY
Grounded Theory (GT) was proposed in 1967 by
both sociologists, Glaser and Strauss, as an
alternative to traditional scientic method that it
normally consists of problem formulation, denition
and verication of the hypothesis and conclusion.
GT, in turn, does not start from any hypothesis.
In this work was employed the following steps of
GT for the guidelines preparation for choosing
visualization techniques, based on GT versions of
the authors: Rodon and Pastor (2007) and
Orlikowski (1993), which are illustrated in Figure 1
and described as follows.
Figure 1: The Grounded Theory Process followed.
Data collection - in this stage, the literature
was used as a data source, in which
information about visualization were
selected and analyzed in order to replace
original data could be obtained from
interviews, questionnaires and forms (Dick,
2005).
Theoretical sampling - it is done alongside
with data collection, gathering all analyzed
data and comparing them with new
informations.
Open coding - from theoretical sampling the
data are classied according to their
similarities. Each class is identied for a
code which, in this case, represents the
parameters to be considered to choose
visualization techniques. It was used the key
point coding method (Allan, 2003).
Memoing - consists of analyst’s annotations
about encoding process and the analyzed
data, providing extra information for
construct theory.
Axial coding - corresponding to arrange the
categories dened in open coding and the
concepts connecting them for contextualize
the theory. Thus, in this step the guidelines
are dened from the association between the
identied parameters and the categories of
visualization techniques.
Diagramming - comprises constructing a
diagram to illustrate the concepts and
categories in order to facilitate the
understanding of the theory or phenomenon
that concludes the study.
Theory generation - this work has as resulted
guidelines for choosing visualization
techniques based on parameters that
inuence this choice according to the
characteristics of the data, being the
guidelines considered as the generated
theory in this step.
Theoretical saturation - when new data will not
inuence the organization and structure of
categories and concepts previously dened,
it is an indication that the theoretical
saturation point was reached and,
consequently, the theoretical sampling
represents the scope of research.
The identied parameters through open coding
process are described in next.
3 GETTING THE PARAMETERS
The parameters to consider in select visualization
techniques emerged from open coding by analyzing
the literature related to visualization techniques, in
which we used the key points encoding method. The
result is illustrated in Table 1.
In the rst column of this table, the expressions
was taken from the related works, whose references
are in the next column, for each one was assigned
the concepts, described in the third column. Thus it
was possible to identify the parameters: data type,
task type, volume, dimensionality and position of the
attributes in the graph, which compose the aspects to
be considered in the decision to adopt visualization
techniques to represent data.
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Table 1: Key point coding method applied on collected data.
Key points References Code
Visualization techniques can be classified, among other criteria,
by data type
Shneiderman (1996)
Freitas et al. (2001)
Keim (2002)
Data type
Task type is one of the aspects considered in classification of
visualization techniques, which provides means of interation
between the analyst and the display
Shneiderman (1996)
Keim (2002)
Pillat et al. (2005)
Task type
Visualization techniques are subject to some limitations, such as
the amount of data that a particular technique can exhibit
Keim e Kriegel (1996)
Oliveira e Levkowitz (2003)
Rabelo et al. (2008)
Volume
Visualization techniques can also be classified according to the
number of attributes
Shneiderman (1996)
Grinstein et al. (2001)
Keim (2002)
Oliveira e Levkowitz (2003)
Dimensionality
In some category of visualization techniques, distribution form of
attributes on the chart can influence the interpretation about the
representation, such as correlation analysis, in which the relative
distance among the plotted attributes is relevant for observation
Ankerst (2001)
Oliveira e Levkowitz (2003)
Inselberg (2008)
Klippel et al. (2009)
Positioning of
attributes
4 GUIDELINES FOR SELECTION
OF VISUALIZATION
TECHNIQUES
Axial coding is the next step after the identication
of the parameters. It was done based on analyzes
about the relationship between the parameters and
categories of visualization techniques, according to
the classication suggested by Keim (2002), who
distinguishes five classes of techniques: (1) standard
1D-3D graphics; (2) iconographic techniques; (3)
geometric techniques; (4) pixel-oriented techniques;
and (5) based on graphs or hierarchical techniques.
Standard graphics are commonly used in statistic
to view an estimate of certainty about a hypothesis
or the frequency distribution of an attribute or to
view a data model. In iconographic techniques data
attributes are mapped into properties of an icon or
glyph, which vary depending on the values of
attributes. In geometric techniques,
multidimensional data are mapped into a two-
dimensional plane providing an overview of all
attributes. In pixel-oriented techniques, each value
of attribute is mapped to a pixel color and it is
placed on the display screen, divided into windows,
each corresponding to an attribute. In the end, they
are arranged according to different purposes (Keim,
2000). Data with a naturally structure of
relationships among its elements, hierarchical or
simple network, may be represented by hierarchical
or graph-based techniques.
It was chose some visualization techniques of
each category to illustrate the analysis of the
parameters. Among standard charts, it was selected:
the Histogram; the Box Plot; the Scatter Plot and the
Contour Plot. Among Icon-based: the Chernoff
Faces; the Star Glyphs and the Stick Figure. Among
Geometrically transformed displays: the Scatter Plot
Matrix and the Parallel Coordinates. Among Pixel-
oriented displays: the Query-dependent and the
Query-independent techniques. Among Graph-based
or Hierarchical: the Graph; the Cone Tree; the
Treemap; the Dimensional Stacking and the Mosaic
Plot. Next, each parameter is analysed with the
techniques mentioned above.
4.1 Analysis on the Data Type
Parameter
Techniques of standard 1D-3D category generally
represents from one to three attributes and are used
for analysis of quantitative data in most cases. All
charts considered in this class are able to display
quantitative data. To represent qualitative data,
alternative techniques are more limited. From the
selected techniques of this class only the Histogram
is able to plot qualitative data (Myatt, 2007).
Iconographic techniques are more appropriate for
quantitative data, because icon features vary with the
values of represented attributes. In Chernoff faces,
the shapes of each facial properties are changed; in
Star Glyph, the components of the star are modied;
in Stick Figure, the format of segments are different
according to the value of attributes.
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Geometric techniques are more exible, being
able to represent quantitative and qualitative data.
This applies to the Parallel Coordinates technique,
which can display attributes of these two data types.
Due to the scatterplot matrix is formed by a set of
scatterplots, it is more suitable for continuous
quantitative data.
In literature are found examples of usage of
pixel-oriented techniques on quantitative data.
Query-independent techniques were applied to
represent temporal data, and query-dependent
techniques are commonly used to represent
continuous quantitative data. Keim (2001) states
that they are not recommended for displaying
qualitative data.
Hierarchical or graph-based techniques are ideal
for displaying data when they have a structure of
relationships among themselves or with a structure
of hierarchy or simple network.
4.2 Analysis on the Task Type
Parameter
Generally, some techniques are better for certain
tasks than others. A task type execution depends if it
is implemented by the tool in use according to the
goals in improve the exploitation data activity.
Task type refers to activities that user or analyst
can perform according to goals in the use of a
graphical representation as noted in the literature
(Keim, 2002; Shneiderman, 1996; Pillat et al.,
2005). For practical purposes, the most common
tasks were considered in this work, such as:
Overview data: view the whole data collection;
Correlation among attributes: the degree of
relationship among variables can reveal
patterns of behaviour and trends;
Identication of rules, standards and important
characteristics;
Clusters identication: attributes with similar
behaviour;
Outliers detection: data set with atypical
behaviour in comparison for the rest of data.
Standard 1D-3D techniques serve, in general, to
view an estimate of certainty about a hypothesis or
the frequency distribution about an attribute, such as
the usage of histogram. This class also provides
graphs to make comparisons and data classications
(in this case, can be used box plot), and also to
determine the correlation between attributes.
Different statistical graphs can be used in data
analysis, in order to discover patterns and structures
in data and identify outliers that can be observed, for
example, through using box plot or scatterplot.
Iconographic techniques represent each data
entry individually, allowing verication of rules and
behaviour patterns of the data. Icons with similar
properties can be recognized and thus form groups
and it be analysed in particular. A representation
with a discrepant format if compared to the other
may characterize an outlier.
Geometric techniques provide a good overview
of the data, assigning no priorities to represent its
attributes. Furthermore, verication of correlation
among them may be more discerning when using
techniques of this class, such as the scatterplot
matrix. This category of techniques also allows the
identication of patterns, rules and behaviours and
may also detect outliers, characterized by behaviours
outside the common standard. The analyst may
choose to analyse a group of data that can be
detached from the tool in use but, in principle,
groups are not immediately identied by techniques
of this class.
Pixel-oriented techniques can be used in the
analysis of relationships among data attributes, to
find data cluster, so rules and patterns may be
identied through observing the correlations among
them.
Hierarchical techniques are useful for
exploitation of data arranged in a hierarchical or
simple relationship. Through techniques of this class
is possible to obtain an overview of the data
structure and analyse the relationship among the
elements. Techniques of this category also allow
grouping data, such as Treemaps (Shneiderman,
2006).
4.3 Analysis on the Volume and
Dimensionality Parameters
Implementations of visualization techniques must
take in consideration the limits of dimensionality
and volume of data to hold in way that the tool be
capable of providing a clear overview of data to the
analyst.
Standard graphics has low dimensional, because
they are intended to represent data with one to three
attributes. In addition, they support the view of a
small volume of data because, in general, they come
from statistical studies, resulting of a sample or of
percentages.
Iconographic techniques are able to handle a
larger number of attributes in comparison to the
standard graphics; however, the visualization
generated is best for a small amount of data due to
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the space occupied by the icons in the screen. This is
the same statement found in (Rabelo, 2008), in
which the iconographic techniques evaluated (Star
glyphs and Chernoff Faces) were classied as low
scalability (support to display an amount of data).
Geometric techniques, in turn, may work with an
increased number of dimensions and volume when
compared to standard 1D-3D graphics and
iconographic techniques. But they are outweighed
by the pixel-oriented techniques for their capability
to represent the largest volume.
Hierarchical techniques or graph-based
techniques are usually used to represent the
relationship among data, regardless of
dimensionality, which can be high or low, but have
the same space constraints like that presented by
iconographic techniques, being the visualization
clearer if the amount data is not bulky.
However, visualization tools can offer features
like zoom, select, lter, among others, to improve
the interactivity with the visualization, mitigating the
limitations of each technique.
4.4 Analysis on the Positioning of
Attributes Parameter
Although it is not a parameter directly linked to the
characteristics of data, it is an important factor in
visual data exploration for some techniques as,
among others, Treemaps (Shneiderman, 2006),
Mosaic Plots (Hofmann, 2008), Dimensional
Stacking (LeBlanc et al., 1990). This parameter
depends on the technique or tool used to generate the
visualization, which should allow the change of the
positions of the attributes in the graph, producing
different views that can reveal new patterns.
In general, for 1D-3D standard graphics,
positioning of attribute do not change the
interpretation of results due to the low
dimensionality of the data that might be represented.
Moreover, the goal of using techniques of this class
is to analyze the behavior of a given attribute, or the
correlation among two or three attributes.
Stick Figures is an example in which the position
of the attributes can inuence the visual data
exploration according to the icon type used, derived
from the variation of the mapping of data attributes
into icon properties (Pickett and Grinstein, 1988).
Chernoff faces, in turn, have a xed structure for
its icon, since it corresponds to the human face
characteristics and thus, the change of the
positioning of attributes is not a relevant aspect for
this technique.
But there are studies about which icon properties
may be more representative for the interpretation of
results, such as the eyes size and the shape of the
face are aspects that draw attention (Morris et al.,
2000; Lee et al., 2003). Likewise it is for Star Glyph
technique, for which once established the order of
the best mapping of attributes (Peng et al., 2004;
Klippel et al., 2009), it remains the same for all the
icons representing a record data per star.
In the works of (Inselberg, 2008) and (Wegman,
1990), it is explained how the position of the
attributes in the graph may inuence the correlation
detection in Parallel Coordinates. Scatteplot Matrix
is, on the other hand, composed of a set of
scatterplots, for this reason nor is inuenced by the
change of attributes positioning, since their main
objective is to evaluate the correlation between
attributes.
Keim (2000) presents techniques for the
placement of pixels on the display, which can
inuence the interpretation of the visualization to
identify patterns and relationships among the
represented attributes.
The query-independent technique, for example,
may have the pixels arranged by recursive pattern
technique. When using the query-dependent
technique, the pixels can be arranged in the window
using spiral technique (Keim, 1997).
Hierarchical techniques or graph-based
techniques are in general inuenced by the attributes
positioning, due to its elements naturally hold a
relationship structure, therefore, the assignment of
variables in the graph should be made carefully,
especially when there is a hierarchy between the
elements. The exception is for the Cone Trees
technique, which represents a dened tree structure
(as les and directories structures in a hard disk),
providing only interactive features such as animation
to navigate among the tree nodes (Cockburn and
McKenzie, 2000; Robertson et al., 1991).
5 CONCLUSIONS
The Grounded Theory provided a methodology for
the identication of the parameters and guidelines
for choose visualization techniques, set forth through
the stages theoretical sampling, coding,
diagramming and formulation of the theory.
During the development of this work, ve
parameters were identied: data type, task type,
volume and dimensionality of the data and position
of the attributes in the graph. Subsequently these
parameters were analyzed in relation to the
GUIDELINES FOR THE CHOICE OF VISUALIZATION TECHNIQUES APPLIED IN THE PROCESS OF
KNOWLEDGE EXTRACTION
187
categories of visualization techniques distinct among
1D-3D standard graphics, iconographic techniques,
geometric techniques, pixel-oriented techniques, and
hierarchical techniques or graph-based techniques.
Through analysis of relationship among the
parameters and the visualization techniques, it was
observed that each technique type have a certain
conguration of parameters that reect the
characteristics of data and the objectives of the use
of visualization.
Data type must be the rst parameter to be
considered. It is the type of data that determines
what kind of visualization technique can be a priori
used. Qualitative data, for example, will be hardly
understood if they were represented by a technique
developed to represent quantitative data and vice
versa. Furthermore, it was veried in this study that
there are more options for visualization techniques
to represent quantitative data than qualitative data.
The task type to be performed corresponds to the
goals of the analyst during the data exploration. In
literature are found classications of visualization
techniques based on this parameter. For tasks related
to statistical analysis, for example, the graphics 1D-
3D may be sufcient; for tasks of correlation
verication may be used visualization techniques of
geometric category, and so on.
Both volume and dimensionality of data are
limiting factors for visualization techniques.
Although most of them supports multidimensional
data, usually these techniques differ in the ability to
display a certain amount of dimensionality and
volume of data.
This is the case of categories of techniques
iconographic, geometric and pixel-oriented.
However, other ways of interaction can be used
during the visual exploration to minimize these
limitations, for example, the functions of zooming,
selection and lter.
The positioning of the attributes is a factor more
dependent on visualization technique to be used and,
hence, on the tool that implements it. For some
techniques such as parallel coordinates and star
glyphs, positioning of attributes is important for
discovery new patterns or behaviors. In the case of
parallel coordinates, positioning of attributes
inuences the way data are displayed in polygonal
lines; in star glyphs technique, the order of
distribution of attributes to the icon properties can
ease grouping task, considering that arranging them
in different orders may generate diverse icon
formats.
Besides the parameters, another important point
to consider is the analyst’s familiarity with the data
analyzed. This is what will awaken new interests or
stimulate the users curiosity during data exploration,
forming new hypotheses that can be veried by
means of visualizations, or simply comparing the
results generated by the graphical representations.
It should be noted that the guidelines were
established based on the strongest features identied
during the coding phase. This does not mean the
invalidation of the use of a visualization technique
for other purposes that differ from those established
by the guidelines.
Therefore, visualization is a benecial tool in
understanding the knowledge, which may be
achieved through data mining algorithms or by
visual exploration performed directly on the data.
ACKNOWLEDGEMENTS
This work was supported by the Fundação
Araucária.
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