An Optimized and Secure Association Rule Mining Algorithm in Parallel and Distributed Environment (International Journal of Engineering and Computer Science, 2018)
Data Mining is a subfield of Artificial Intelligence which is mainly used for extraction of hidden predictive information from large available databases. To Handle Large Data set is very tedious and complex task. The solution to this problem is to apply distributed or parallel approaches. The field of distributed data mining has therefore gained increasing importance in the last decade. In Distributed data mining Security is the major problem with respect to association rule mining projects. Association rule mining is become one of the core data mining tasks and has attracted tremendous interest among data mining Researchers.  The Apriori algorithm by Rakesh  Agarwal  has  emerged  as  one  of  the  best  Association  Rule  mining  algorithms.  It also serves  as  the  base algorithm  for  most  parallel  algorithms.  The performance of data mining algorithm can be accelerated from O(N) to O(N/k) with parallelism, where N = number of data records and k =number of nodes in distributed system. By using various cryptographic techniques and Method interesting associations and patterns between variables of big database can be observed securely. This system addresses the problem of Secure  association rule mining  over the horizontally distributed database system. In Current technique for association rule mining huge processing time and also  high threat from various attacks. The proposed system can provide security at the time of mining data from various data sources and also reduce computation time by using parallel programming environment like OpenMp. The goal of proposed approach is to find all association rule with  maintain  support s and confidence c and minimize the information disclosed about the private databases held by those player .This system is based on distributed mining algorithm, K&C and AES algorithm. Distributed mining algorithm used here is the distributed version of apriori algorithm. With proposed approach speed up is acquired using Parallel Computing while preserving the privacy of the data .