<![CDATA[Frequent Closed Itemset Mining is an important approach in discovering hidden patterns inlarge-scale data. The CEG&REP (Concurrent Edge Prevision and Rear Edge Pruning)algorithm has previously been proven to improve the efficiency of the pattern mining processthrough parallel edge projection mechanisms and selective pruning of sequence graphstructures. However, the search space exploration can still be very large when the datasetcontains many items, high sequence lengths, or complex pattern variations. This research is animprovement of CEG&REP through the integration of constraint-based mining, namely theapplication of various types of constraints that can direct the mining process only to relevantpatterns. Three main types of constraints are introduced: temporal constraints (time-basedconstraints), length constraints (pattern length constraints), and item constraints (itemexistence or attribute constraints). This integration allows the pruning process to occur earlier,reducing the exploration of irrelevant branches, and improving the quality of the resultingpatterns. This approach aims to make CEG&REP more adaptive, efficient, and suitable forvarious application domains such as user activity logs, IoT sensor data, retail transactions, andbioinformatics analysis.]]>
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