Data mining and data analytic are the key components in the field of data science and applied research. Specifically, generating a rule or pattern in mining still suffer in the issue of high consumption of memory storage. Recent research is focusing on interestingness measure to define a significant threshold value that refers to minimum support or confidence level of certain items. Whereas the incremental or parallel approach to reduce memory consumption are some of the initiatives from active researchers. The research purpose is to develop a performance enhancement in Incremental Eclat (iEclat) model by embedding CRS measure in mining of infrequent itemset. The CRS measure acts as an interestingness measure (filter) in iEclat model that comprises of i-Eclat-diffset algorithm, i-Eclat-sortdiffset algorithm and i-Eclat-postdiffset algorithm for infrequent (rare) itemset mining. The idea of association rule mining is to discover relationships among sets of items (itemsets) in a transactional database. The task of association rule mining is to discover if there exist the frequent itemset or infrequent patterns in the database and if any, an interesting relationship between these frequent or infrequent itemsets can reveal a new pattern analysis for the future decision making. Regardless of frequent or infrequent itemsets, the persisting issues are deemed to execution time to display the rules and the highest memory consumption during mining process. CRS-iEclat engine is proposed to overcome the said issues. Prior to experimentation, results indicate that CRS-iEclat outperforms iEclat from 54% to 100% accuracy on execution time (ET) in selected database as to show the improve of ET efficiency.