Edwige Fangseu Badjio, François Poulet
ESIEA Recherche,
Parc Universitaire Laval – Chan
38, r
ue des Docteurs Calmette et Guérin
53000 Laval France
Keywords: HCI, visual data mining, software quality, utility, usability, acceptability, evaluation, GQM model
Abstract: The main purpose of this work is to integrate HCI (Human Co
mputer Interaction) requirements in visual
data mining tools engineering. We present the definition of metrics/measurements in order to improve the
quality of those tools at all the steps or after the development process. On the basis of these
metrics/measurements, we have derived a questionnaire for the evaluation of the utility, the usability and the
acceptability of visual data mining environments. A case study enables us to concretely materialize the
contribution of the measurements and also to detect and explain (design and usage) errors.
According to (Fayyad et al. 1996) data mining is the
non-trivial process of identifying valid, novel,
potentially useful and ultimately understandable
patterns in data. Visual data mining consists in the
use of visualization as a communication channel for
data mining. For (Wong, 1999), visual data mining
lies in tightly coupling the visualizations and
analytical process into one data mining tool that
takes advantage of the strengths of all worlds.
The standards ISO 9241, ISO/IEC 9126, ISO
3407, (Nielsen, 1993), (Bastien et Scapin, 1999)
criteria propose attributes which characterize
software quality in terms of usability. These
attributes include: the facility to learn the software,
the user’s satisfaction, comprehensibility, efficacy,
operability, attractiveness etc... How can we
concretely incorporate these various attributes in a
visual data mining environment? We know that
finding and fixing a software problem after delivery
is often 100 times more expensive than finding and
fixing it during the requirements and design phase
(Boehm et Basili, 2001).
From the user’s point of view, the interface is the
ost important element of the software because it is
the user’s mediator with the system for the
achievement of his task (Costabile, 2000). We try to
bring replies to this question. Indeed, some research
works (Kolski et al., 2001) insists on the fact that
when the software is highly interactive, an adapted
methodological step is essential, the traditional
cycles of the software engineering being insufficient.
We have defined a set of metrics/measurements
ommendations) for the improvement of the
quality of visual data mining tools. The application
of these recommendations allows the correction of
errors likely to occur with less expense than at the
end of the design process. Based on the formal
model GQM (Basili et al., 1994), the proposed
approach also allows an evaluation after the tool
design and development in order to explain the
minor and major errors detected.
The overview of this paper is the following: first,
we e
xplain the theoretical foundations of our work
before the measurement definition and the
questionnaire presentation, section 4 presents a case
study followed by the conclusion and future works.
The utility of a visual data mining tool relates to the
adequacy existing between the functions provided
by the system and those necessary to the user in
order to achieve the visual mining tasks assigned to
The usability is the quality of hardware or
ware which is easy and pleasant to use and to
understand, even by somebody who has little
Fangseu Badjio E. and Poulet F. (2005).
In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 98-103
DOI: 10.5220/0002547700980103
knowledge in data processing. Usability is a critical
quality of the success of a software product.
The acceptability relates to the adequacy
between the decision to use the visual data mining
environment and the compatibility of the tool for the
user and the organization to which he belongs.
A measurement is a support allowing answering
a variety of questions related to the software
development process. It also allows evaluating and
measuring the quality of the end products.
A metric is a mathematical number that shows a
relationship between two variables.
The theoretical base of this research work is
software quality studies. Quality is the aptitude of a
product or a service to satisfy the user's needs. One
of the earlier works of the software quality field is
McCall’s (McCall et al, 1977) model which counts
around fifty criteria allowing the expression of
software quality in general. Another set of software
quality factors was carried out by (Boëhm, 1978).
These factors are related to the software
functionalities and performances from the software
engineering point of view and the usability. From
the human machine interaction and software
engineering point of view, the awaited aptitudes of
the software analysis and different level of
evaluation: the satisfaction of the user's needs,
reliability, interoperability, conformity with the
standards and a good ratio cost/performance. More
recently, case studies such as (Nielsen, 1993),
(Bastien et Scapin, 1999) stress the ergonomic
evaluation and improvement of the user interface
much more. Cognitive psychology work related to
data visualization (Healey, 1996) proposes also
interesting primitives in such cases.
The principal advantage of ergonomic inspection
is its level of detail. Indeed, this method guarantees
an exhaustive analysis of the overall software.
For the needs of data mining algorithm's
technical aspect evaluation, data sets repository
creation efforts was the object of projects such as
UCI (Blake et al, 1998). In visualization for mining
purpose, Grinstein's research work (Grinstein et al,
1997) allowed the evaluation of data representation
methods. The evaluation criteria were: the memory
size of the computers, their execution speed and
their graphical capacities. It should be noted that we
have taken into account all the various details
explained in this section for the development of our
evaluation method so that it is as most complete as
For the visual data mining tools measurements
specification, our formal framework is GQM
"Goal/Question/Metrics" (Basili and al. 1994)
model. GQM is a support making it possible to
better clarify the objectives to be reached, a set of
attributes are identified via questions. Downwardly,
measurements are defined. For the evaluation and
the explanation, one proceeds in an ascending way.
If one is located in the measurement
specification model (figure 1), our objective will be
to find the means of specifying each usability
criterion described by the various ISO standards in
order to apply it to visual data mining specificities.
For example in order to develop the user’s guidance
(goal), the question will be how to advise, direct our
end-users? One of the answers is to provide decision
support or recommendation for the selection of the
data mining algorithm to be performed.
Figure 2 describes the measurement definition
process. First, we have done an analysis of end users
followed by a task analysis, the usability goals
definition in a visual data mining context. More
description of the new evaluation approach is the
subject of the following section.
Goal 2
Goal 1 Goal 3
Figure 2: Measure definition process
Measure Measure
Figure 1: GQM Model (Basili et al 1994)
Metrics/measurements are important for software
engineering activity. Developers use metrics for
controlling software quality throughout the project
life cycle. By using software measurements, the
managers can see measurable attribute of the
software quality. Customers look for the
measurements in order to determine the quality of
the products. Maintainers use metrics as an indicator
for reusability or reengineering.
3.1 User analysis
The end-user of visual data mining tools could be
the data specialist. He has a basic knowledge in data
analysis. This data mining approach has been
developed for the need of integrating the user in the
knowledge discovery process in order to combine
the human potentialities of judgement with the
computer calculation capacities.
3.2 Task analysis
(Ankerst, 2000) describe a task model for visual
data mining field. This model includes data
visualization and application of mining algorithms
steps. Ankerst’s model more explicitly reduced the
visual data mining to the use of data visualization
during the pre-treatment (data exploration phases),
the treatment (knowledge discovery step) or the post
processing (knowledge representation step). This
model does not correspond to our visual data mining
model detailed by the figure 3. The user can
visualize the data in pre-treatment. The fundamental
difference is the fact that the user interacts with a
graphical representation (chart) of that data in
knowledge discovery in the data. The data model
(knowledge) is built in an interactive way.
3.3 Usability goals: G of GQM
The ISO 9241, ISO/IEC 9126, ISO 13407 standards,
(Bastien et Scapin, 1999) and (Nielsen, 1993) work
propose metrics for software usability improvement.
(Bastien et Scapin, 1999) metrics are used like
support. These metrics constitute the G (goals) side
of the GQM model. These metrics are:
Guidance: means implemented in order to
advise, direct, inform and lead users.
Workload: the interface elements must reduce
the users’ perceptive load, just as in the
improvement of the dialogue efficacy.
Explicit control: possibility for the system to
take into account the explicit actions of the users and
the control they have, relating to their actions
Adaptability: system capacity to react according
to the context, the needs and the user preferences.
Error management: means allowing to reduce
errors and to correct them when they occur.
Compatibility: agreement between the users
characteristics, the tasks and their organisation.
In the following sections, we will use the GQM
model (figure 1) to formalize these usability needs
according to the visual data mining field.
3.4 Questions to answer for
improving usability and utility: Q
of GQM
After having defined our GQM model goals, the
second stage consists of the definition of a set of
questions whose responses will allow the
achievement of the goals. The task analysis enabled
us to define also a set of questions related to utility
(technical quality of tools) and to mix them with
usability questions. Due to space constraints, the set
of questions can not be presented here.
3.5 Usability and utility measures: M
of GQM (top level)
We studied the visual data mining process in order
to bring responses relating to usability’s and utility’s
questions. These responses include the specificities
of visual data mining tools. For example, for the
users’ workload reduction, it is judicious to give
them the possibility of choosing a visualization
method among several possible alternatives.
Several analysis methods can be included in a
data mining environment. For the usability
(guidance) improvement, the developers have to
advise users in the choice of the most suitable
User in
Figure 3: our visual data mining task model
method. The execution of an analysis method is time
consuming. If the method execution does not finish
under good conditions it is necessary to envisage the
re-use of training data. In order to take into account
user preferences, developers must also envisage
several visualisation methods.
3.6 Usability and utility measures: M
of GQM (lower level)
There are several methods for software evaluation
needs. We have chosen a questionnaire for the
setting up of our evaluation method because its does
not require any user competence. The questionnaire
has six topics. It provides a methodology to verify if
the desired quality requirements (G of GQM) have
been satisfied. It is also used to conduct tests,
conduct reviews and audits and to review software
program design. Table 1 presents rating levels in
order to fill in the questionnaire. The best level
possible with our rating level is 5, the worst possible
level is 1.
Mark Rating
1 Absent, Strongly disagree, Extremely
(difficult, confusing, boring)
2 Poor, Somewhat disagree, Somewhat
(difficult, confusing, boring)
3 Fair, Neutral
4 Good, Somewhat agree, Somewhat
(easy, clear, fun)
5 Excellent, Strongly agree, Extremely
(easy, clear, fun)
The first topic of our questionnaire is the visual
data mining tool technical quality, (table 2) it
relates to technical aspect: data-processing
capabilities, operating system, speed, compatibility,
etc… The perception of the tool technicality allows
the measurement of the tool power given the
capacities offered. The evaluation on this level refers
to the adaptability to the task, with the
implementation precision, the capacity of knowledge
prediction. The measurements based on technology
also allow the evaluation of the degree with which
the system can handle data of variable sizes.
The Scenario topic refers to the execution
details of the visual data mining software. These
details particularly relate to the interaction quality
and time necessary for this purpose.
The topic Interface presentation model (IPM)
refers to the elements of the graphic interface (color,
typography …) and allows the consideration of
aesthetics and attracting aspects of the tool.
The Visualization topic relates to the relevance
of the charts and their structure compared to the
mining objectives of the user and his profile. It is
question here of seeing in which cases the charts
used facilitate knowledge perception and
The Usability topic is based on general
recommendations for the ergonomic design of the
The topic User allows defining explicitly the
user profile and takes it into account for the software
improvement. User topic also makes it possible to
have an overall perception of the use of the visual
data mining tool, of its design features, such as the
adaptability and the adequacy of the system, of its
communication and control facilities, its robustness
and its effectiveness, its simplicity of
implementation and comprehension, as well as its
convivial and personalized character. This topic
makes it possible to understand the total reaction of
a user compared to the mode and structure of the
interaction, the communication means used,
flexibility and assistance.
Table 1: Rating levels
Table 2: Technical quality
1 2 3 4 5
Heterogeneous data
Algorithms diversity
Data models validation
Results appearance
Models exportation
Re use of training data
Treatment of data sets
with large dimension
In this section, we will apply the questionnaire in
order to evaluate the quality of two methods
included in a visual data mining tool. These methods
are CIAD (Poulet, 2001) and PBC (Ankerst et al.,
1999). We listed four visual data mining methods.
But two of these methods are not free.
These evaluated methods relate to visual data
mining: the use of two dimensional visualizations
techniques and the capacity of the system to treat
complex interactions between all the possible pairs
of attributes. The test is performed by four
autonomous users (a researcher and three PhD
students in visual data mining field). This evaluation
aims to detect and explain design problems (usage
4.1 Evaluation tasks
For our evaluation needs, the prescribed task is the
interactive construction of decision trees starting
from representations of small data sets like Iris (150
records, 4 numerical attributes, 3 classes), glass (214
records, 6 numerical attributes, 9 classes),
ionosphere (351 records, 2 numerical attributes, 34
classes) data sets from the UCI (Blake et Merz,
1998). The decision trees allow partitioning a great
quantity of data in small segments by application of
a series of decision rules. They are very much used
in data mining. Their coupling with data
visualization leads to tools for interactive
construction of decision trees in which the end-users
could be the data field specialists. The quality of the
models resulting from the visual data mining
depends on the quality of the method used. It is thus
necessary to develop tools useful, usable and
acceptable for data field expert’s users. The
following paragraph describes the interpretation of
the obtained results.
4.2 Evaluation results
Table 3 presents the mean ratings of the case study
evaluation questionnaires. The first six lines
represent the topics of the evaluation questionnaires.
We present also five sub topics (the last five lines of
the table 3) which we considered to be more
The evaluators agree on the fact that the tools are
very useful. The error’s treatment is done in a
relevant manner; CIAD and PBC are thus
convenient. Also, for an autonomous user, the tool is
very easy to understand and to use. The interfaces
presentation models are well developed. The
elements disposal on the screen is very good;
graphics and colours are well used. CIAD allows the
training data set re-use, which makes it possible to
reduce the users’ workload. Such is not the case for
The evaluators also agree on the fact that the
installation of these tools is not obvious. These tools
are designed for experimental purposes. Only one
algorithm and only one visualization method are
implemented for PBC and CIAD. The users can not
assess prefered analysis methods or visualization
tools. It is not possible to access various data set
formats. For CIAD and PBC, the users are not
directed (guided), also, it misses the on line help, the
contextual menus, the user manual. The results of
this evaluation allow the designers of CIAD and
PBC to develop the aspects related to the usability
(assistance modules, user manual, several
alternatives possible with regard to data analysis
methods and data visualization, cognitive aspects of
visualization for data mining, user preferences) of
these tools and thus to work on their acceptability.
Table 3: Mean rating scores
Guidance 1 1.5
Workload 1 1.5
Explicit control 1 1.5
Adaptability 1 1
Error management 3.5 3.75
Compatibility 2 3
Usefulness 5 5
Ease of use 3.25 3.75
Assistance 1 1
Learning ability 3.5 3.5
Installation 1 1
In order to avoid the redevelopment of design which
generates wastes of time and high cost of production
without however guaranteeing performance, we
study the development of useful, usable and
acceptable visual data mining software. To this end,
we have drawn up a list of criteria having to be taken
into account for a development of reliable tools of
this type and for their evaluation. Also, in order to
cover all the aspects of that type of tool analysis, we
listed six topics. Those topics are defined in the form
of a tree structure including the principal topic,
under topics or meta-criteria and criteria. These
various criteria have been used for the evaluation of
CIAD and PBC, modules dedicated to the interactive
construction of decision trees. The evaluation
allowed us to use one of the advantages of our
analysis approach. It allows a thorough evaluation of
the software (interface, technical quality, ergonomic
(usability), visualization and scenario). The design
problems are thus discovered on all the tool levels.
The end users of visual data mining tools could
be data domain specialists. They don’t have
knowledge needed for the selection of the best tool
available for the treatment of their data. From the
method presented above, we work now on the
definition of a preference index for the
recommendation of visual data tools to these users.
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