An optimal method to update association rules based on Immune Algorithm in data mining

Association rules mining is one of the most important and well-researched techniques of data mining, which aims to induce associations among sets of items in transaction databases or other data repositories. There have been a number of successful algorithms developed for frequent itemsets identifying and association rules updating in very large databases. Currently Apriori algorithms play a major role in identifying frequent item sets and deriving rule sets out of it. However using conjunctive nature of association rules and the single minimum support factor are not adequate to derive useful rules effectively. Hence in this paper, an optimal method to update association rules based on Immune Algorithm (IA) is proposed. Combined with IA in biology, key factors and the process of the algorithm can be taken a considerable optimization. The reported experiments results present that the proposed method shows better results than Apriori algorithm and mitigate the performance degradation.