Directed clonal selection algorithm for associative classification
Lihua Zhao, Jin Pan · International Journal of Modelling Identification and Control · 2013
In this paper, we present an directed clonal selection algorithm (DCLONALG) for mining association rules. Different from the traditional evolutionary algorithms, DCLONALG firstly scans dataset one time to find frequent rules with only one item. Then, these items were used to generate new rules and the mutation operation was limited in it. We evaluate the performance of the proposed DCLONALG approach for AC and the performance results have shown that the proposed approach is efficient in dealing with the problem on the complexity of the rule search space. At the same time, good classification accuracy has been achieved. This is especially important for mining association rules from large datasets in which the search space of rules is huge.