When choosing between decision trees and clustering, remember that decision trees are themselves a clustering method. The leaves of a decision tree contain clusters of records that are similar to one another and dissimilar from records in other leaves. The difference between the clusters found with a decision tree and the clusters found using other methods such as K-means, agglomerative algorithms, or self-organizing maps is that decision trees are directed while the other techniques I mentioned are undirected. Decision trees are appropriate when there is a target variable for which all records in a cluster should have a similar value. Records in a cluster will also be similar in other ways since they are all described by the same set of rules, but the target variable drives the process.