Multiple Item Support Constraints Based Frequent Pattern Mining Using Dynamic Prefix Tree

The structural complexity of the pattern mining algorithm depends on the types of datasets. Even though they are highly connected, it can be intriguing to identify patterns in some application areas where they do not usually occur. FP tree construction practice with traversal of conditional pattern base with conditional FP Tree, with path traversing, to address the issue of massive memory and time usage. The creation of a non-recursive single-label dynamic prefix tree with a rule generation method utilizing multiple-item support restrictions is the paper’s significant contribution. The effectiveness of our proposed method is also compared to the FP tree and state-of-the-art TIS tree on various datasets in terms of time and memory complexity.