An Interactive Visualization Methodology For Association Rules

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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 9, No. 2, February 2011

AN INTERACTIVE VISUALIZATION
METHODOLGY FOR ASSOCIATION RULES

MOHAMMAD KAMRAN Dr. S. QAMAR ABBAS
Research Scholar, Integral University, Kursi Road, Professor, Ambalika Institute of Management &
Lucknow, India Technology, Lucknow, India
E-mail: mkamran_lko@hotmail.com, Dr. MOHAMMAD RIZWAN BAIG
Professor, Department of Information Technology, Integral
University, Lucknow, India

Abstract- The task of the knowledge discovery and data mining much higher degree of confidence in the findings of the
process is to extract knowledge from data such that the resulting exploration. This fact leads to a high demand for visual
knowledge is useful in a given application. Obviously, only the exploration techniques and makes them indispensable in
user can determine whether the resulting knowledge satisfies this conjunction with automatic exploration techniques.
requirement. Moreover, what one user may find useful is not
necessarily useful to another user. Visual data mining tackles the The main contribution in this study is addressing the
data mining tasks from this perspective enabling human capabilities and strengths of data mining technology in
involvement and incorporating the perceptivity of humans. The identifying placement of students and to guide the teachers to
objective of this paper is to present the students performance
concentrate on appropriate attribute associated and counsel the
through visualization mining method on data coming from
educational institute. Such method together with the novel
students or arrange for suitable placement to them. In this
visualization technique described here allows the analyst to work, we propose a dynamical framework for association rule
explore data and view significant differences among performance mining that integrates interactive visualization techniques in
values of students. The results are immediately presented in a order to allow users to drive the association rule finding
graphical form and the user is allowed to change settings in order process, giving them control and visual cues to ease
to allow him or her to iteratively explore the data and find some understanding of both the process and its results.
useful knowledge.
II. ASSOCIATION RULE MINING (ARM)
I. INTRODUCTION
Association Rules Mining (ARM) [2] can be divided into
For data mining [1] to be effective, it is important to two sub problems: the generation of the frequent itemsets
include the human in the data exploration process and lattice and the generation of association rules. The complexity
combine the flexibility, creativity, and general knowledge of of the first sub problem is exponential. Let |I|=m the number
the human with the enormous storage capacity and the of items, the search space to enumerate all possible frequent
computational power of today’s computers. Visual data m

exploration aims at integrating the human in the data itemsets is equal to 2 , and so exponential in m [2]. Let I ={a1,
exploration process, applying its perceptual abilities to the a2, … , am} be a set of items, and let T ={t1, t2, … , tn} be a set
large data sets available in today’s computer systems. The of transactions establishing the database, where every
basic idea of visual data exploration is to present the data in transaction ti is composed of a subset X I of items. A set of
some visual form, allowing the human to get insight into the items X I is called itemset A transaction ti contains an
data, draw conclusions, and directly interact with the data.
Visual data mining techniques have proven to be of high value itemset X in I, if X ti. Several ARM published papers are
in exploratory data analysis and they also have a high potential based on two main indices which are support and confidence
for exploring large databases. These huge databases contain a [2]. The support of an itemset is the percentage of transactions
wealth of data and constitute a potential goldmine of valuable in a database where this itemset is one subgroup. The
information. As new courses and new colleges emerges, the confidence is the conditional probability that a transaction
structure of the educational database changes. Finding the contains an itemset knowing that it contains another itemset.
valuable information hidden in those databases and identifying An itemset is frequent if support (X)  minsup, where minsup
and constructing appropriate models is a difficult task. Data is the user-specified minimum support. An association rule is
mining techniques play an important role at each stop of the
strong if confidence(r) minconf, where minconf is the user-
information discovery process and visual data exploration
specified minimum confidence. Left part of an association rule
usually allows a faster data exploration and often provides
is called antecedent and right part is called conclusion. Our
better results, especially in cases where automatic algorithms
motivations are described hereafter.
fail. In addition, visual data exploration techniques provide a

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III. MOTIVATION We focus on visualization during the post processing stage
The number of generated rules is a major problem on and we are interested by ARM. Independently of both context
association rules mining. This number is too significant and and task, ARM has a main drawback which is the high number
leads to another problem called Knowledge mining. The of generated rules. Several works on filtering rules were
human cycles spent in analyzing knowledge is the real bottle proposed and a state of the art was presented in [3]. Although
neck in datamining. This issue can limit the final user‘s reducing the whole of generated rules significantly, this
expertise because of a strong cognitive activity. To solve it, number remains however important. Expert must be able to
visual datamining became an important research area. Indeed, easily interact with an environment of datamining in order to
extracting relevant information is very difficult when it is more easily understand the displayed results. This point is
hidden in a large amount of data. Visual data mining attempts essential for the global performance of the system. Visual
to improve the KDD process by offering adapted visualization tools for association rules were proposed to reduce this
tools which allow tackling various known problems. Those cognitive analysis but they remain limited [3].
tools can use several kinds of visualization techniques which V. VISUAL ASSOCIATION RULE MINING
allow simplifying the acquisition of knowledge by the human
mind. It can handle more data visually and extract relevant Various works already exist to help expert analysis in text-
information quickly. mode [4]. Several works on visual rules exploration were
published [2], [5], [6], [7]. The main beliefs of our interactive
Indeed, in most real life databases, thousands and even ARM are described hereafter. All these tools use several
millions of high-confidence rules are generated, among which methods which are textual, 2D or 3D way. The choice of one
many are redundant. In this paper, we are interested in the of them proves to be a difficult work. Moreover, their
most used kind of visualization categories in data mining, i.e., interpretations can vary according to the expert. Each one of
use visualization techniques to present the information catched these techniques presents advantages and drawbacks. It is
out from the mining process. Visualization tools became more necessary to take them into account for the initial choice of the
appealing when handling large data sets with complex representation. The effectiveness of these approaches is
relationships, since information presented in the form of dependent on the input data files. These representations are
images is more direct and easily understood by humans. understandable for small quantities of data but become
Visualization tools allow users to work in an interactive complex when these quantities increase. Indeed, particular
environment with ease in understanding rules. In a based information can not be sufficiently perceptible in the mass.
tabular view of association rules, all strong rules are The common limitation of all the representations is that if they
represented as in a tabular representation format (rule table), in are global, they quickly become unreadable (size of the objects
which each entry corresponds to a rule. All rules can be in 2D, occlusions in 3D) and if they are detailed, they do not
displayed in different order, such as order by premise, provide an overall picture on these data to the expert.
conclusion, support or confidence. This helps users to have a
clearer view of the rules and locate a particular rule more VI. RELATED WORK
easily. Traditionally, many simple methods are designed to render
small amount of data or statistical features of big data sets,
IV. VISUAL DATA MINING
such as histogram, pie, tree, etc. To visualize more complex
The rise of KDD revealed new problems as knowledge data, modern scientific visualization utilizes more advanced
mining. These large amounts of knowledge must be explored techniques. Visualization techniques, such as EXVIS [8],
with specific advanced tools. Indeed, expertise requires an Chernoff Faces [9], icons [10] and m-Arm Glyph [11], are
important cognitive work, a fortiori, a harmful waste of time often called glyph-based methods. Glyphs are graphical
for industrial. Extracting nuggets is a difficult task when entities whose visual features, such as shape, orientation, color
relevant information is hidden in a large amount of data. In and size, are used to encode attributes of an underlying
order to tackle this issue, visual datamining was conceived to dataset, and glyphs are often used for interactive exploration
propose visual tools adapted to several well-known KDD of data sets [12]. Glyph-based techniques range from
tasks. These tools contribute to the effectiveness of the representation via individual icons to the formation of texture
processes implemented by giving understandable and color patterns through the overlay of many thousands of
representations while facilitating interaction with experts. glyphs [13]. Chernoff used facial characteristics to represent
Visual data mining is present during all KDD process: information in a multivariate dataset [14]. Each dimension of
upstream to apprehend the data and to carry out the first the data set encodes one facial feature, such as nose, eyes,
selections, during the mining, downstream to evaluate the eyebrows, mouth, or jowls. Glyphmaker proposed by Foley
obtained results and to display them. Visual tools became and Ribarsky visualize multivariate datasets in an interactive
major components because of the increasing role of the expert fashion [14]. Levkowitz described a prototype system for
within KDD process. Visual datamining integrates concepts combining colored squares to produce patterns to represent an
resulting from various domains such as visual perception, underlying multivariate dataset [15]. In [10] an icon encodes
cognitive psychology, visualization metaphors, information six dimensions by six lines of different colors within a square
visualization, etc. icon. In [13] Levkowitz describes the combination of textures

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and colors in a visualization system. The m-Arm Glyph by students. With the help of this technique, educational
Pickett and Grinstein [11] consists of a main axis and m arms, institutions can.
and the length and thickness of each arm and the angles i. Segment the student database to create student
between each arm and main axis are used to encode different profiles.
dimensions of a data set. [6] describes a glyph-based system
for large high dimensional datasets. These techniques are ii. Conduct analysis on a single student segment for a
incapable of visualizing large amount of high dimensional data single factor. For example, the institution can perform
because: in-depth analysis of the relationship between
attendance and academic achievement.
 Lack of human computer interaction.
 Lack of integration with other data mining and iii. Analyze the student segments for multiple factors
knowledge discovery (KDD) tools. using group processing and multiple target variables.
For example, ―What are the characters shared by
VII. PROPOSED WORK students who drop out from colleges?
Nowadays, higher educational organizations are facing a iv. Perform sequential (over time) basket analysis on
very high competitive environment and are aiming to get more student segments. For example, ―What percentage of
competitive advantages over the other business competitions. high attendance holders also achieved in academic
These organizations should improve the methodology of side also?
teaching, placement and counseling of students. They consider
students and teachers as their main assets and they want to B. Developing new strategies
improve their key process indicators by effective and efficient Teachers can increase the placement percentage by
use of their assets identifying the most lucrative student segments and organize
the training sessions accordingly. The results may be affected,
Students’ academic performance is critical for educational if teachers do not offer the right kind of training to the right
institutions because strategic programs can be planned in student segment at the right time. With data mining operations
improving or maintaining students’ performance during their such as segmentation or association analysis, institutions can
period of studies in the institutions. The academic now utilize all of their available information for betterment of
performance in this study is measured by certain attribute as students.
indicated in Table 1. This study presents the work of data
mining in predicting the final placement of students. This TABLE I ATTRIBUTE LIST
study applies association rule mining technique to choose the
best prediction and analysis. The list of students who are
ATTRNAME ATTR Possible
predicted as likely to drop from the selection criterion by data
Values
mining is then turned over to teachers and management for Enrolment No. ENR Yes, No
direct or indirect intervention.
Attendance ATT Poor, Good,
For example, let us consider the transaction database of Average
few students from Students’ repository of institute which 10+2 Grade INT A, B, C
shows the students general and academic grades in different Area of EXP M,C,E
courses they enrolled for during their years of attendance in expertise
the institution. Student performance score is basically Gender G M, F
determined by the sum total of the continuous assessment and
the examination scores. In most institutions the continuous Fund F P, S, F
assessment which includes various assignments, class tests, Student STD ME, CS, IT
Department
group presentations is summed up to weigh 30% of the total
score while the main semester examination is 70%. To Activities ACT A, B, C
performed by
differentiate different students’ performances we have selected the
different attributes as attendance, Mark, Activity etc. .as student
shown in table 1.
Percentage of PSA A, B, C
Educational institutions with Association rule mining can practical
predict the student's performance more accurately, which in session
Exercise given ET A, B, C
turn can result in quality education. by
teacher
A. Student Level Analysis Average mark ER A, B, C
Successfully training the student requires analyzing the of the
experience
data at the student level. Using the associated discovery data report
mining technique, educational institutions can more accurately Final mark MARK A, B, C
select the kind of training to offer to different kinds of Evaluation EVL A, B, C

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VIII. SYSTEM ARCHITECTURE is not distributed uniformly within data values. A user would
like a visualization system to be able to show these knowledge
Request differences clearly. To be specific, two differences of same
amount in data values may not necessarily be rendered by the
VB Applications ODBC DBMS
Result identical difference in visual elements on the screen. Instead
the difference representing more information should be
Main Window displayed more significantly to get attention from a viewer.
Interactive Visualization Model
LogIn Rule Generator Visualization
Stored Load the dataset
Procedure

3-D Rule Table Find cluster for each individual dimension
Visualization

Perform association and transformation according to rule
Client Machine Database Server

Figure 1. System Architecture Render Data

The system architecture is shown in Figure. The database
resides in the server machine. The stored procedures (Oracle) Change
reside in the server side. Our VB application runs in the client Association Change Association
machine. It consists of several modules: LogIn, Rule Step
Generator, and Visualization module. LogIn module is used to
connect to the database server. Rule Generator is used to
mining the association rules given the information provided by
Transformation
the user. Visualization module consists of two sub-modules Change
Step transformation
Rule table and 3-D visualization. These modules can be
accessed using the Main window.
Knowledge Extraction Stage
Figure 2. Visualization Model
Rendering millions of icons is computationally expensive,
and interpretation and analysis to be performed by the user is In Figure 2 we give an interactive visualization model which
even harder. A visualization system has to provide not only a has the following properties:
“loyal” picture of the original dataset, but also an “improved”
picture to a viewer for easier interpretation and knowledge 1) Interaction: It is clear that integration of domain knowledge
extraction. Integration of analysis functionality is important to a visualization system is very important due to the
and necessary to help the viewer to extract knowledge from problem of non-uniform knowledge distribution. To a
the display. The basic requirement about a visualization visualization system integration of domain knowledge can
system as: be achieved by choosing proper association function and
transformation function during visualization process.
“Different data values should be visualized differently, However, there is no universal technique for all fields, data
and the more different the data values are, the more sets or users, and a visualization system should be
different they should look”. interactive and provide a mechanism for views to adjust or
But what a viewer wants to find with a visualization change association and transformation functions during
system is not data values themselves, instead, it is the visualization process. And each data set or field has to be
information or knowledge represented by data values. So, the studied individually and visualized interactively before its
above requirement can be better stated as: important information can be revealed, which can only be
performed by viewers or domain experts. By interaction a
“Different information should be visualized differently, viewer can guide a visualization system step by step to
and the more different the information is, the more display what he is interested in more and more clearly.
different it should look”.
2) Correctness: We propose the following criteria for
To help a viewer on knowledge extraction a visualization “correct” visualization:
system has to deal with the problem of non-uniform
knowledge/information distribution. It is common in some a) If possible a visualization system should show
data sets or fields that a small difference of a value could mean different dimensions of a data set differently
a big difference, which means the knowledge and information

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through different visual objects or visual elements The main window consists of the menu, toolbar, and a text
of one visual object. area. The user can connect to different databases, here Oracle
through the connect sub-menu and disconnect from the same
b) The more different the values are, the more
through the disconnect sub-menu. Under the Generate Rule
differently they should be rendered. Since we may
menu, the user can choose generate rules. The operations of
not know the distribution of a dataset, assigning
rule generation and rule visualization are mainly done through
data values to visual elements/properties may not
the menu.
make full usage of available visual
elements/properties, a clustering step is preferred. We use VB standard EXE as the software development
tool to implement our project. VB provides an Integrated
c) The more different the information represented by
Development Environment (IDE), which makes interface
data values are, the more differently they should be
design, program debugging very efficiently. The menu can be
rendered. A distinguished visual difference
implemented using the Menu Editor. All the objects in the
between different information can help viewers
main window can be designed visually.
better, which can be achieved by interaction
between a visualization system and viewers. In this
interaction process, viewers can fine tune the
transformation between data values and visual
elements, and domain knowledge is obtained and
reflected through a more customized display.
3) “Maximizing” rule: To optimize the rendering quality, the
maximal range of visual objects/elements should be used as
default settings.
IX. IMPLEMENTATION METHODOLOGY
At the beginning of any mining task, the system acquires
the support for each attribute category defined at discretization
step during preprocessing phase of a generalized composite
record in the corresponding cluster. Figure 3 depicts the user
interface screens that acquire these supports. In order to show
how our technique has enhanced the rule generated, we
conducted the following experiment steps: Run the system and
give variable support for each attribute category based on the Figure 4. Menu Editor
user interest.
After the user chooses “Connect” menu item, a Login
1) Count the number of rules generated and the number window will be brought up. Login module of Association Rule
of used premises in these rules. Software is described in fig. 5. After the user provided all the
2) Rerun the system and give equal support for all needed information, the user can choose to “Connect” to the
attributes categories. DBMS
3) Count the number of rules and the premises used in
these rules. Private Sub cmdOK_Click()
4) Examine the quality of rules generated in each case a.connect txtUserName.Text, txtPassword.Text
by comparing the number of rules and premises used. If Loginsucceeded Then
Form1.mnuconn.Enabled = Not Loginsucceeded
Form1.mnudisc.Enabled = Loginsucceeded
Form1.Toolbar1.Buttons(1).Enabled=Not Loginsucceeded
Unload Me
Form1.Show
End If
End Sub

Figure 5. Login Module
Figure 3. Interface Screen

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Rule Generator graph of our choice by clicking on any of the option button
For each input data set, some parameters have to be available in the visualization effect window, as shown in the
specified by the user for the association rule generation. This figure 6.
kind of information can be arranged in the concerned table, Some of the generated rules are given in Table 2 in a form
because the data is not always in the same table, and that is understandable by humans. In Table 2, the first column
sometimes it is needed to obtain the data from two or more represents the rule number, the generated rules are presented
different tables, the user should have the ability to select in the second column, the number of the students who
multiple tables as the data source in the procedure. The user successfully satisfy the rules is given in the third column,
may also want to specify the lowest support and confidence and the number of attributes contained in the rule is given in
value to get the interested association rules. The value of stop the last column. The table shows the rules in a descending
level is used to let the user decide that after how much passes order depending on the number of the students who
that the user wants the rule generation needs to be canceled. successfully have satisfied the rule. This ordering helps in
The information is taken from the transaction table and the determining the most significant rule. For the generated rules,
user can click the “Generate Rules” menu button to begin to the longest rule consists of 10 attributes while the shorter
merge the data from different tables. Then the association rule rule contained only 3 attributes.
generation algorithm will be called to generate the rules.
TABLE 2 GENERATED RULES

1- Select the mining task and consequently the appropriate
Rule # Rules # Obj # Attrib
cluster
2- Get the confidence threshold for generating a rule (this IF ENR = Y, ATT = A, INT=A, G
means that the rule will only be generated if the number of = M, STD=IT, ACT=A, PSA=A,
occurrences of records described by this rule divided by the 7 ET=A, ER=B, MARK=A THEN 13 10
total number of records in the cluster greater than the EVL = A
given confidence threshold)
3- Construct a matrix (calculated relative weight) with IF ENR = Y, ATT = B, INT=A, G
number of rows equal to the number of attributes (m) and = F, STD=IT, ACT=A, PSA=A,
number of columns (n) equal to the maximum number of 3 ET=A, ER=B, MARK=A THEN 9 10
categories of a certain attribute EVL = A
4- Using the appropriate cluster, fill in the calculated relative IF ENR = Y, ATT = B, INT=A, G =
matrix with the relative weight of each attribute category in M, STD=CS, ACT=A, PSA=C,
this cluster ET=C, ER=B, MARK=B THEN
5- Compare the calculated relative weight with the user given 11 9 10
EVL = B
support and mark irrelevant attributes categories.
6- For each generalized composite record do IF ENR = Y, ATT = C, INT=A,
7- For each generalized composite record attribute do { if the G = M, F=SC, STD=ME,
attribute category is irrelevant then mark it as irrelevant 17 ACT=A, PSA=B, MARK=B 8 9
copy relevant attributes category into a new table} THEN EVL = A
8- Group similar rows in the new table and calculate a IF ENR = Y, ATT = A, INT=B, G
confidence value for this grouped records = F, ACT=A, PSA=A, ET=A,
9- Generate rules ER=B, MARK=B THEN EVL =
9 5 8
B
Proposed algorithm
IF ENR = Y, ATT = C, G = M,
The algorithm is based on well known existing techniques ACT=B, PSA=A, ER=B,
14 MARK=A THEN EVL = A 4 7
to obtain association rules as Apriori algorithm. This
algorithm is modified to enable a user to control and impose
IF ENR = Y, ATT = C, MARK=C
his area of focus during knowledge discovery steps in order to 3 THEN EVL = C 3 3
overcome the loss of information problem and to enable
him/her to generate rules that he/she is interested in. The We implemented the mapping of intermediate rule table
proposed algorithm solved this problem by allowing the user into the format that the user can understand easily. A
to define the relative weight or support of each attribute visualization module that includes rule table and 2-D, 3-D
interval category such that the mining algorithm could graphics was developed to help the user get the interested
generate rules using this attribute interval category only if this information easier through sorting, and filtering functions.
support is satisfied. Besides the performance our software can access the data
The generated rules can be visualized in either the table format stored in multiple data tables through ODBC such as Oracle.
or 2D, 3D format by selecting the appropriate visualization Our visualization module uses ‘rule-item’ relationship so that
Menu Item. As we execute the program the title screen comes it can display more rules at one time. In additional, the rule
into action which is shown in fig. 3. sorting and filtering ability of our visualization module gives
the user more flexibility and efficiency in managing and
Further we click on the Visualization Menu to get different understanding the association rule. In our implementation, we
graph related to Association rules. We can further select the store the generated rules in the database. Once the rules are

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We conclude by remarking that visualization of association AUTHORS PROFILE
mining results in particular and data mining results in general Mohammad Kamran is a Software Developer. His primary interests lay in the
is a promising area of future work. Educational, research, areas of data mining and association rules. Nowadays, he is a research scholar
government and business institute can benefit significantly in Computer Science at Integral University. His paper summarizes the current
state of his thesis work on the field of "study of association rules in large
from the symbiosis of data mining and information database.
visualization disciplines.

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