Extracting high utility Itemsets from transactional data samplesdenotes to the production of high utility Itemsets that generates higher profit. Mining of Closed High-Utility Itemsets (CHUIs) functions like a dense and lossless depiction of High Utility Itemsets (HUIs). In addition, CHUIs and its generators are also beneficial in the recommendation and analytical systems. Even though existing approaches have proposed efficient methodologies for the extraction of CHUI and generators, those techniques pre-dominantly used single utility threshold values and single support values. Thus, in this methodology, we suggested an improved association rule miningapproach using multiple minimum support values and utility values for the extractionof CHUI and High Utility Generator (HUG). The extraction is performed through the construction of Lattice for the generated HUIs swiftly to minimize the consumption period where the size of the exploring domain is very large. The performance of the proposed methodology is tested using three available datasets such as foodmart, retails, and chess. The suggested approach has lesser runtime and memory usage exhibited by experimental outcomes when matched with the prevailing approaches.