Data Mining Application in Advertisement Management of Higher Educational Institutes

International Journal of Computer-Aided technologies (IJCAx) Vol.1,No.1,April 2014
43
Data Mining Application in Advertisement
Management of Higher Educational Institutes
Priyanka Saini
M.Tech(CS) Student, Banasthali University Rajasthan
Sweta Rai
M.Tech(CS) Student, Banasthali University Tonk,Rajasthan
Ajit Kuamr Jain
Assistant Professor, Computer Science Department Banasthali University
Tonk,Rajasthan
Abstract
In recent years, Indian higher educational institute’s competition grows rapidly for attracting students to
get enrollment in their institutes. To attract students educational institutes select a best advertisement
method. There are different advertisements available in the market but a selection of them is very difficult
for institutes. This paper is helpful for institutes to select a best advertisement medium using some data
mining methods.
Keywords
Data Mining, KDD, Data Mining in Education, Association Rule Mining, Apriori Algorithm
1. INTRODUCTION
Data mining is a process of analyzing and extracting the hidden information from large amounts
of data. Today educational data is increasing rapidly due to information and communication
technologies but this data is not in proper format. It contains many hidden information so data
mining is required here. Different data mining techniques are used to get hidden and important
information from this data.KDD (Knowledge discovery in database) is also called as data mining
that extract useful and unknown information from data [1]. Its application is in science,
commerce field and other fields. Use data mining in education is an interesting research area and
it is called Educational Data Mining (EDM).In EDM data mining techniques are applied on
student data of educational institutes.EDM is a process of extracting unknown patterns from
educational environment for improved performance [2]. EDM is linked with some fields like as
Artificial Intelligence (AI), Machine Learning/Information Technology, Database Management
System, Statics and Data Mining.EDM is an interdisciplinary area for research that provides
teaching and learning process’s knowledge [3].This study helps to find out the best method for
advertising the educational institutes by which student attracted easily at the time of admission.

44
2. DATA MINING AND KDD PROCESS
Data mining refers to extracting or “mining” knowledge from large amounts of data [1]. Data
mining is the powerful technology for analyzing & extraction of hidden predictive information
from the data. It helps for making a better decision. Data mining is one of the steps in KDD
process. Knowledge discovery (KDD) aims at the discovery of useful information from large
collections of data [4] .Data mining is used for processing data & this data is applied in many
areas to obtain useful knowledge from the data [5].
2.1 KDD
KDD refers to Knowledge discovery in database means a process of searching knowledge in large
data. Data mining is used as a step in KDD process.KDD process is shown in figure1.KDD
process includes following steps:
Step1: Data collection
Step2: Database cleaning
Step3: Integration of data
Step4: Selection and transformation of data
Step5: Use data mining techniques
Step6: Patterns are evaluated
Step7: Representation of knowledge
Figure 1: KDD Process
2.2 Association Rule Mining
It is used to discover relationships between attributes and items such as the presence of one
pattern implies the presence of another pattern. Association Rule is a popular technique for
market basket analysis because all possible combinations of interesting product groupings can be
explored.

45
2.2.1Apriori Algorithm
Apriori algorithm is used for association rule’s mining. It counts the support of item sets and
generates candidate set using breadth-first search approach. It finds a frequent item sets by testing
the groups of candidates against the data. When no further successful extensions are found the
algorithm stops.
3. DATA MINING IN EDUCATION
Now-a-days there are growing research field in educational data mining. Educational Data
Mining is a research region with the application of data mining in which information is generated
from educational institutes. This research field continues to grow, and many data mining
techniques have been applied in it. The goal of each research is to convert raw data into a
meaningful form to make a better decision. Educational data mining explores the educational data
to better understand the students[6]. It uses many techniques such as decision tree, neural
networks to discover knowledge [7].The educational data mining research is increasingly
growing, starting by organizing workshop since 2004.There are many research papers on
educational data mining in which higher education related problems are discussed with their
possible solutions using data mining techniques. In 2007 many literature reviews of the EDM are
provided by Romero and Ventura [7].Educational data mining (EDM) helps the student for
getting better result. It helps also management for maintaining education infrastructure, increase
in the area of interest & courses. The educational systems face many problems that overcome by
data mining techniques. It helps to the institution by giving the student guidance and helps to
management in increasing the performance by using data mining techniques. It can help to
provide a better quality of education.
4. LITERATURE SURVEY
Data mining is helpful to finding hidden information from student database. There is a lot of
research papers regarding educational data mining field. Yadav and Pal [8, 9] give a study on
educational data mining and provide data mining technique’s list.
Yadav, Bharadwaj and Pal [10] obtained the students data such as attendance, seminar,
assignment marks and class test to predict the end semester performance using three algorithms
ID3decision tree, C4.5 and CART and result shows that CART gives better result for
classification of data.
Pandey and Pal [11] show their study using association rule analysis to find the student interest of
choosing class language. In this paper they use seven different interestingness measures. Their
result concluded that student has shown their interest in mix mode class language.
Bharadwaj and Pal [12] use the classification decision tree technique to evaluate student’ end
semester performance, this study helps to identify the dropouts and students who require special
attention and teacher advising.
AI-Radaideh. et al [13] presents a classification based model for student performance prediction
using ID3 algorithm,C4.5 and Naïve Bayes algorithm but decision tree had better results.

46
K.S. Priya and A.V.S. kumar [14] use a classification approach that extracts the knowledge from
student end semester marks. Bhise R.B,Thorat S.S and Supekar A.K. [15] performed data mining
process on the student database using clustering-K-mean algorithm. Abeer Badr El Din Ahmed
and Ibrahim Sayed Elaraby [16] use decision tree technique for predicting the student final grade.
5. EXPERIMENTAL WORK
In this study the data is collected through the questionnaire survey at the institute. There are 500
questionnaires are collected. This questionnaire includes student personnel and academic
information. This study is helpful for finding a better advertisement for institutes so this
questionnaire includes a important question “How They knows about this university”?Table1
shows different advertisement methods that are available in questionnaire’s field. Table 2 shows
different combination of advertisements occurrences that occur while responding the
questionnaire field.
Table1: Different advertisement methods
Advertising methods Code
s
Answers
Friends FR 309
Family FA 17
Internet Search IS 24
Online Advertising OA 8
Newspaper NE 8
Others OT 5
Table2: Different combination of advertisements
Relations Occurrence
s
Friends and Family(FRFA) 10
Friends and Internet Search(FRIS) 6
Friends and Online Advertising(FROA) 2
Friends and Newspaper(FRNE) 3
Friends and Others(FROT) 0
Family and Internet Search(FAIS) 0
Family and Online Advertising(FAOA) 0
Family and Newspaper(FANE) 7

47
Family and Others(FAOT) 0
Internet Search and Online Advertising(ISOA) 4
Internet Search and Newspaper(ISNE) 2
Internet Search and Others(ISOT) 0
Online Advertising and Newspaper(OANE) 2
Online Advertising and Others(OAOT) 0
Newspaper and Others(NEOT) 0
Total counts(support) 36
Analysis of Support and Confidence Value
The support value of a data item indicates the percentage of database transaction in which that
data item appears. Confidence value measures the rule’s strength. A small support and large
confidence value are meaningful. Support and confidence analysis is shown in Table3.Support
and Confidence values are calculated as:
Support= Occurrences/Total Support
Confidence= Support(X,Y)/Support(X)
Table 3: Support and confidence analysis of different relations
Relations Support
=occurrences/Total
Support
Confidence =support(X,Y) /
support(X)
FA->FR 0.28 0.59
IS->FR 0.17 0.25
OA->FR 0.06 0.25
NE->FR 0.08 0.375
Friends and Others 0 -
Family and Internet
Search
0 -
Family and Online
Advertising
0 -
NE->FA 0.19 0.875
Family and Others 0 -
IS->OA 0.11 0.17
NE->IS 0.06 0.25
Internet Search and
Others
0 -
OA->NE 0.06 0.25
Online Advertising and
Others
0 -

48
Figure 2: Advertisement method’s links
Cosine Value Analysis
Cosine value occurs between 0 and 1.If its value is close to 1 then a good correlation occurs
between X and Y. Cosine analysis formula defined as:
Cosine(X->Y) = p(X,Y) / sqrt(p(X)*p(Y))
Table 4: Cosine analysis of different relations
Relatio
ns
Cosine
value=(p(X,Y)/sqrt(p(X)*p(
Y)))
FA-
>FR
0.13
IS->FR 0.06
OA-
>FR
0.04
NE-
>FR
0.06
NE-
>FA
0.60
IS->OA 0.28
NE->IS 0.14
OA-
>NE
0.25
From table 3 result it can conclude that 28% students have answered with family and friends.
Since 28% is largest support then it is a better advertisement method and its confidence value is
59% shows that if 59% of the time family occurs then friends also occurs in response.Table3
FRFA FRIS FROA FRNE ISOA ISNEFANE OANE
FR FA IS OA NE

49
and Figure 2 concluded that the all advertisement methods are linked to either friend
advertisement or newspaper advertisement medium.
In figure 2 it is shown that friends and newspaper have five edges. In table 4, cosine values are
calculated for all advertisement mediums and it concludes that newspaper → family has good
relation to each other but others relations are not so close to each other.
Apriori algorithm implementation
Table 5: Advertisement Methods data set
Relations Code Occurrence
s
(Support
count)
Friends FR 309
Family FA 17
Internet Search IS 24
Online Advertising OA 8
Newspaper NE 8
Others OT 5
Friends and Family FRF
A
10
Friends and Internet Search FRIS 6
Friends and Online
Advertising
FRO
A
2
Friends and Newspaper FRN
E
3
Family and Newspaper FAN
E
7
Internet Search and Online
Advertising
ISO
A
4
Internet Search and
Newspaper
ISNE 2
Online Advertising and
Newspaper
OAN
E
2
Step 1: In this step the table 5 is scanned by algorithm and obtained information generates
candidate item set C1 (shown in table 6).
Step 2: In this step, compare C1 candidate item set with minimum support count that generate
frequent item set L1

50
Apriori algorithm C1 Join L1 transformation
Table 6: Generated Candidate item set C1
Code Occurrences
(Support count)
FR 309
FA 17
IS 24
OA 8
NE 8
OT 5
Minimum support count=5
Step 3: In step 3, L1 Join L1 provides L2 frequent item set and candidate item set C2 it is called
joining process.
Candidate item set C2 generation by L1 Join L1 transformation
Step 4: In this step the table 9 is scanned by algorithm and compare C2 candidate item set with
minimum support count that generate frequent item set L2
Table 9: Candidate set C2 with Support Count
Code Support
Count
FR,FA 10
FR,IS 6
FR,OA 2
FR,NE 3
FR,OT 0
FA,IS 0
FA,OA 0
FA,NE 7
FA,OT 0
IS,OA 4
= >Minimum support (5)
Table 7: Generated frequent item
sets L1
Code Occurrences
(Support count)
FR 309
FA 17
IS 24
OA 8
NE 8
OT 5
Code Occurrences
(Support count)
FR 309
FA 17
IS 24
OA 8
NE 8
OT 5
L1 Join L1
Table 8: Generated Candidate set C2
L1*L1 L1-1 L1-2
FR,FA FR FA
FR,IS FR IS
FR,OA FR OA
FR,NE FR NE
FR,OT FR OT
FA,IS FA IS
FA,OA FA OA
FA,NE FA NE
FA,OT FA OT
IS,OA IS OA
IS,NE IS NE
IS,OT IS OT
OA,NE OA NE
OA,OT OA OT
NE,OT NE OT
= > Minimum
support (5)
Table 10: Generated frequent item
sets L2
Code Support count
FR,FA 10
FR,IS 6
FA,NE 7

51
IS,NE 2
IS,OT 0
OA,NE 2
OA,OT 0
NE,OT 0
Step 5: In step 5, L2 Join L2 provides L3 frequent item set and candidate item set C3 (shown in table 11).
L2 Join L2
Candidate item set C3 generation by L2 Join L2 transformation
Step 6:
• The subsets of (FR,FA,IS) are (FR,FA),(FR,IS)and(FA,IS).(FA,IS) is not a member of
L2. It is not a frequent item set. Therefore, remove (FR,FA,IS) from C3.
• The subsets of (FR,FA,NE) are (FR,FA),(FA,NE)and(FR,NE).(FR,NE) is not a
member of L2. It is not a frequent item set. Therefore, remove (FR,FA,NE) from C3.
• The subsets of (FR,IS,FA,NE) are
(FR,IS),(FR,FA),(FR,NE),(IS,FA),(IS,NE)and(FA,NE) .(FR,NE),(IS,FA),(IS,NE) are
not a members of L2. It is not a frequent item set. Therefore, remove (FR,IS,FA,NE)
from C3.
• These item sets are pruned according to pruning property because its item sets are not
frequent ,C3 is null, so final frequent item set is L2.If the minimum confidence is 50%
then Confidence (FA->FR) and Confidence (NE->FA) is maximum so these are the
frequent item sets (shown in table 13).
Code Support count
FR,FA 10
FR,IS 6
FA,NE 7
Table 11: Generated Candidate
set C3
L2*L2 L2-1 L2-2
FR,FA,IS FR,FA FR,IS
FR,FA,NE FR,FA FA,NE
FR,IS,FA,NE
FR,IS
FA,NE
Table 12: Frequent item set L2
Code Support Count Confidence
FRFA 10 0.59(59%)
FRIS 6 0.25(25%)
Table 13: Frequent item sets
Code Confidence
FRFA 0.59(59%)
FANE 0.875(87%)

52
Figure 3: Analysis chart that shows different advertisement methods(answered given in questionnaires)
Figure 4: Analysis chart that shows different advertisement methods (according to data mining analysis)
6. CONCLUSION
This study looks for a better advertisement method. Fig.3 shows that most students have
answered with friends (309 times) in the questionnaire but in this study data mining techniques
are applied and this analysis concluded that the Family and Newspaper could be the best
advertising method(shown in Fig.4). Table 1 shows that most students have answered with
friends (309 times) but with data mining technique’s analysis it is concluded that the Friends
and Family, Family and Newspaper could be the best advertisement methods because these
advertisement methods are linked with all advertisement methods. After applying apriori
algorithm on the data set, Table 13 shows frequent item sets (FRFA,FANE) that concluded that
Friends and Family, Family and Newspaper is the best advertisement methods. This study is
helpful for educational institutes to making a good advertisement strategy that attracts student
effectively.
0
50
100
150
200
250
300
350
Friends Family Internet
Search
Online
Advertising
Newspaper Others
0
0.2
0.4
0.6
0.8
1
Friends and
Family
Internet
Search and
Friends
Online
Advertising
and Friends
Newspaper
and Friends
Newspaper
and Family
Internet
Search and
Others

53
7. REFERENCES
[1] Han, J., Kamber, M.: Data Mining: Concepts and Techniques,Morgan Kaufmann Publishers
USA,(2000).
[2] A.Y.K. Chan, K.O. Chow, and K.S. Cheung“,Online Course Refinement through Association Rule
Mining”Journal of Educational Technology Systems Volume 36, Number 4 / 2007-2008, pp 433 –
44, 2008.
[3] Romero, C., and Ventura S.(2013), “ Data Mining in Education”. WIREs Data Mining and
Know.Dis.,Vol.3,pp.12-27.
[4] Heikki,Mannila, .Data mining: machine learning, statistics, and databases., IEEE, 1996.
[5] Klosgen, W. & Zytkow,J. (2002), Handbook of data mining and knowledge discovery , Oxford
University Press, New York.
[6] International Educational Data Mining Society www.educationaldatamining.org.
[7] C. Romero and S. Ventura, “Educational data mining: A survey from 1995 to 2005”.Expert Systems
with Applications 33 (2007) 135-146.
[8] S.Pal., “Mining Educational Data to Reduce Dropout Rates of Engineering Students”, I.J.
Information Engineering and Electronic Business
(IJIEEB), Vol. 4, No. 2, 2012, pp. 1-7.
[9] C. Romero and S. Ventura, “Educational data mining: A survey from 1995 to 2005”, expert system
with applications 33 (2007) 135-146.
[10]S. K. Yadav, B.K. Bharadwaj and S. Pal, “Data Mining Applications: A comparative study for
Predicting Student’s Performance”, International Journal of Innovative Technology and
CreativeEngineering (IJITCE), Vol. 1, No. 12, pp. 13-19, 2011.
[11]U. K. Pandey and S. Pal, “A Data Mining view on Class Room Teaching Language”, IJCSI, Vol 8
issue 2 Maerch 2011 pg 277-282, ISSN 1694-0814.
[12]B.K. Bharadwaj and S. Pal. “Mining Educational Data to Analyze Students’ Performance”,
International Journal of Advance Computer Science and Applications (IJACSA), Vol. 2, No. 6, pp.
63-69, 2011.
[13]Q. A. AI-Radaideh, E. W. AI-Shawakfa, and M. I.AI-Najjar,“Mining student data using decision
trees”,International Arab Conference on Information Technology (ACIT’2006), Yarmouk
University, Jordan, 2006.
[14]K.Shanmuga Priya and A.V.Senthil Kumar,” Improving the Student’s Performance Using
Educational Data Mining”, Int. J. Advanced Networking and Applications Volume: 04 Issue: 04
Pages:1680-1685 (2013) ISSN : 0975-0290.
[15]Bhise R.B, Thorat S.S, Supekar A.K, “Importance of Data Mining in Higher Education System”,
IOSR(Journal Of Humanities And Social Science),Volume 6, Issue 6 ,PP 18-21, (Jan. - Feb.2013).
[16]Abeer Badr El Din Ahmed and Ibrahim Sayed Elaraby, “Data Mining: A prediction for Student's
Performance Using Classification Method”, World Journal of Computer Application and
Technology 2(2): 43-47, 2014.

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