Decision trees are a useful tool to help in the extraction of information from databases, but all too often this ability is clouded by the complexity of the tree structure resulting from the decision tree algorithm. Methods such as tree pruning, attribute selection, and most recently, instance selection, currently exist to simplify the decision tree structure. We present an alternative instance selection procedure for simplifying decision trees that improves upon previous methods by increasing the quality of the space to be traversed for finding an acceptably simplified decision tree through the identification and grouping of important instances. Experimental results from this procedure are then presented and compared to decision trees with no prior simplification effort applied. We show that in some cases we are indeed able to identify important group of instances, and subsequently are able to generate a high quality solution space for finding simplified decision trees.