  Gamma-ray bursts provide what is probably one of the messiest of all
astrophysical data sets. Burst class properties are indistinct, as overlapping
characteristics of individual bursts are convolved with effects of instrumental
and sampling biases. Despite these complexities, data mining techniques have
allowed new insights to be made about gamma-ray burst data. We demonstrate how
data mining techniques have simultaneously allowed us to learn about gamma-ray
burst detectors and data collection, cosmological effects in burst data, and
properties of burst subclasses. We discuss the exciting future of this field,
and the web-based tool we are developing (with support from the NASA AISR
Program). We invite others to join us in AI-guided gamma-ray burst
classification (http://grb.mnsu.edu/grb/).
