A Novel Algorithm for Mining Hybrid-Dimensional Association Rules

Association rule mining is a fundamental and vital functionality of data mining. Most of the existing real time transactional databases are multidimensional in nature. In this paper, a novel algorithm is proposed for mining hybrid-dimensional association rules which are very useful in business decision making. The proposed algorithm uses multi index structures to store necessary details like item combination, support measure and transaction IDs, which stores all frequent 1-itemsets after scanning the entire database first time. Frequent k-itemsets are generated with previous level data, without scanning the database further. Compared to traditional algorithms, this algorithm efficiently finds association rules in multidimensional datasets, by scanning the database only once, thus enhancing the process of data mining.