Frequent pattern discovery based on co-occurrence frequent item tree

Existing association rule mining algorithms like a priori, partition algorithm, FP-growth suffers from many problems when mining massive transactional datasets. Some of the major problems are: (i) the repetitive I/O disk scans, (2) the huge computation involved during the candidacy generation, and (3) the high memory dependency. This paper presents the implementation of frequent itemset mining algorithm, COFI, which achieves its efficiency by applying four new ideas. First, it can mine using compact memory based data structures. Second, for each frequent item assigned, a relatively small independent tree is built summarizing co-occurrences. Third, clever pruning reduces the search space drastically. Finally, a simple and non-recursive mining process reduces the memory requirements as minimum candidacy generation and counting is needed to generate all relevant frequent patterns.