Utilising association rule discovery methods to construct classification systems in data mining is known as associative classification. In the last few years, associative classification algorithms such as CBA, CMAR and MMAC showed experimentally that they generate more accurate classifiers than traditional classification approaches such as decision trees and rule induction. However, there is room to improve further the performance and/or the outcome quality of these algorithms. This paper highlights new research directions within associative classification approach, which could improve solution quality and performance and also minimise drawbacks and limitations. We discuss potential research areas such as incremental learning, noise in test data sets, exponential growth of rules and many others.