4.6 ADVANTAGES
Data mining is present in many aspects of our daily lives, whether we realize it or not.
It aects how we shop, work, and search for information, and can even in uence our leisure time, health, and well-being. So data mining is ubiquitous (or ever-present.
Several of these examples also represent invisible data mining , in which smart soft-
MITCOE, Pune. 18 Dept. of Computer Engg.
Student Performance Analysis using Apriori Algorithm ware, such as search engines, customer-adaptive web services (e.g., using recommender algorithms), intelligent database systems, email managers, ticket masters, and so on, incorporates data mining into its functional components, often unbeknownst to the user. [11]
Uses large item set property: Can
To handle such concerns, numerous data security-enhancing techniques have been developed. In addition, there has been a great deal of recent eort on develop- ing privacy-preserving data mining methods. In this section, we look at some of the advances in protecting privacy and data security in data mining. What can we do to secure the privacy of individuals while collecting and mining data?" Many data se- curity enhancing techniques have been developed to help protect data. [2] Databases can employ a multilevel security model to classify and restrict data according to var- ious security levels, with users permitted access to only their authorized level. It has been shown, however, that users executing speci c queries at their authorized security level can still infer more sensitive information, and that a similar possibility can occur through data mining. Encryption is another technique in which individual data items may be encoded. This may involve blind signatures (which build on public key
[5] Chandra, E. and Nandhini, K. (2010) Knowledge Mining from Student Data,
European Journal of Scienti c Research, vol. 47, no. 1
[6] El-Halees, A. (2008) Mining Students Data to Analyze Learning Behavior:
A Case Study, The 2008 international Arab Conference of Information Technology
(ACIT2008) Conference Proceedings, University of Sfax, Tunisia.
[7] Shannaq, B. , Rafael, Y. and Alexandro, V. (2010) Student Relationship in
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Student Performance Analysis using Apriori Algorithm
Higher Education Using Data Mining Techniques, Global Journal of Computer Science and Technology,
[8] Shyamala K. and Rajagopalan S. P., Data Mining Model for a better Higher
Educational System", Information Technology Journal, Vol. 5, No. 3, 2006.
[9] Ranjan J. and Malik K., "Eective Educational Process: A Data Mining Ap- proach", Vol. 37, Issue 4, pp 502-515, 2007.
[10] M.Ramaawami and R.Bhaskaran, "A CHAID based performance prediction model in educational data mining", 2010.
[11] J.Mamcenko, Jelena, Irna Sileikiene, Jurgita Lieponiene and Regina Kulvi- etiene, "Analysis of E-Exam data using data mining techniques", proceeding of 17th international conference on information and software technologies,