Optimization of association rule mining using improved genetic algorithms

In this paper, the main area of concentration was to optimize the rules generated by association rule mining (a priori method), using genetic algorithms. In general the rule generated by association rule mining technique do not consider the negative occurrences of attributes in them, but by using genetic algorithms (GAs) over these rules the system can predict the rules which contains negative attributes. The main motivation for using GAs in the discovery of high-level prediction rules is that they perform a global search and cope better with attribute interaction than the greedy rule induction algorithms often used in data mining. The improvements applied in GAs are definitely going to help the rule based systems used for classification as described in results and conclusions.