[hamner]

There is no such thing as a "best" data mining algorithm. Almost all the advantages you mentioned for decision trees, a form of recursive binary partitioning, applies to a greater extent to Random Forests, which are bootstrapped decision trees that only consider a subset of features at each node.

Examples of domains where decision trees perform poorly include: -Low amount of data -Domains where you have extra knowledge about the data (such as some features coming from certain probability distributions) that you can incorporate into classifiers.

Decision trees work well in a variety of applications, but that does not make them the "best" algorithm, and it is rare that a classical decision tree provides state of the art performance on any given data set. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.122...