An Effectual Algorithm For Frequent Itemset Generation In Generalized Data Set Using Parallel Mesh Transposition

Association mining is one of the most researched areas of data mining and has received much attention from the database community. Association rules are interesting correlations among attributes in a database. It plays an important role in generating frequent item sets from large databases. Many industries are interested in developing the association rules from their databases due to continuous retrieval and storage of huge amount of data. The discovery of interesting association relationship among business transaction records in many business decision making process such as catalog decision, cross-marketing, and loss-leader analysis. It is also used to extract hidden knowledge from large datasets. The Association Rule Mining algorithms such as Apriori, FP-Growth requires repeated scans over the entire database. All the input/output overheads that are being generated during repeated scanning the entire database decrease the performance of CPU, memory and I/O overheads. In this paper, We have proposed An Effectual Algorithm for mining Association Rules in Generalized data set that is fundamentally different from all the previous algorithms in that it uses database in transposed form and database transposition is done using Parallel transposition algorithm so to generate all significant association rules number of passes required is reduced. We will compare proposed algorithm with Apriori algorithm for frequent item sets generation. The CPU and I/O overhead can be reduced in our proposed algorithm and it is much faster than other Association Rule Mining algorithms.