Abstract
Traditional pattern mining algorithms become increasingly inefficient in dynamic e learning environments, where learner interactions accumulate continuously. When new data arrive, these algorithms typically discard previously discovered patterns and recompute everything from scratch—a costly process that this work seeks to avoid. The proposed incremental intelligent maximal frequent itemsets (IIMFIs) framework integrates three complementary mechanisms: incremental maintenance, maximal pattern extraction, and novelty based pruning. Rather than restarting computation with each new batch, IIMFIs stores and selectively updates previously discovered patterns. The novelty threshold \theta controls pruning of highly similar candidates, further reducing the search space. Experiments on the OULAD dataset split chronologically into four batches—demonstrate several advantages. First, IIMFIs compresses the pattern set considerably: at the lowest support threshold, it reduces 2,597 patterns generated by classical algorithms to only 342, achieving a compression factor of 7.6×. Second, incremental updates achieve a 4.2× speedup over full restart and run 28% faster than restarting FP Growth. Third, activating novelty pruning (θ=0.7) improves recommendation precision by 15.5% while maintaining diversity above 0.95. Notably, F1@10 scores remain identical to those obtained from full recomputation, confirming that compactness does not compromise quality. These results demonstrate that IIMFIs provides an efficient and scalable solution for adaptive e-learning environments.