Non-recursive Generation of Frequent K-itemsets from Frequent Pattern Tree Representations

Existing association rule mining algorithms suffer from many problems when mining massive transactional datasets. One major problem is the high memory dependency: gigantic data structures built are assumed to fit in main memory; in addition, the recursive mining process to mine these structures is also too voracious in memory resources. This paper proposes a new association rule-mining algorithm based on frequent pattern tree data structure. Our algorithm does not use much more memory over and above the memory used by the data structure. For each frequent item, a relatively small independent tree called COFI-tree, is built summarizing co-occurrences. Finally, a simple and non-recursive mining process mines the COFI-trees. Experimental studies reveal that our approach is efficient and allows the mining of larger datasets than those limited by FP-Tree