Performing Data Mining in (SRMS) Through Vertical Approach with Association Rules

This system technique is used for efficient data mining in SRMS (Student Records Management System) through vertical approach with association rules in distributed databases. The current leading technique is that of Kantarcioglu and Clifton [1]. In this system I deal with two challenges or issues, one that computes the union of private subsets that each of the interacting users hold, and another that tests the inclusion of an element held by one user in a subset held by another. The existing system uses different techniques for data mining purpose like Apriori algorithm. The Fast Distributed Mining (FDM) algorithm of Cheung et al. [2], which is an unsecured distributed version of the Apriori algorithm. Proposed system offers enhanced privacy and data mining with respect to the Encryption techniques and Association rule with Fp-Growth Algorithm in private cloud (system contains different files of subjects with respect to their branches). Due to this above techniques the expected effect on this system is that, it is simpler and more efficient in terms of communication cost and combinational cost. Due to these techniques it will affect the parameter like time consumption for execution, length of the code is decrease, find the data fast, extracting hidden predictive information from large databases and the efficiency of this system is increased by the 20 %.