Apriori Property Based Algorithm (PApriori) for Finding Frequent Determinant Patterns from High Dimensional Datasets

At the present, due to the developments in Database Technology, massive volumes of data are produced by everyday operations and that they have introduced the necessity of representing the data in High Dimensional Datasets. Discovering Frequent Determinant Patterns and Association Rules from these High Dimensional Datasets has become very tedious since these databases contain a large number of various attributes. For the reason that it generates a very large number of redundant rules that make the algorithms inefficient and it doesn't fit in main memory. in this paper, a new Association Rule Mining approach is given, and it with efficiency discovers Frequent Determinant Patterns and Association Rules from High-Dimensional Datasets. The proposed approach adopts the traditional Apriori algorithm and device a new PApriori algorithm to prune the generated Frequent Determinant Sets effectively. A Frequent Determinant set is chosen if its value is first compared with conviction threshold value and then compared with Support threshold. This double comparison can eliminate the redundancy and generate strong Association Rules. To enhance the mining process, this algorithm additionally makes use of a compressed data structure f_list created from feature attributes selected using Heuristic Fitness Function (HFF) and a Heuristic Discretization Algorithm. It additionally makes use of Count Array (CA) devised as one Dimensional Triple Array pair set to minimize main memory utilization. This comprehensive study shows that the approach outperforms with traditional Apriori and obtains more fast computing speed and at the same time generates sententious rules. Further, the mining methodology is observed to be better in generating strong Association Rules from High Dimensional Databases.