  We use ESX, a product of Information Acumen Corporation, to perform
unsupervised learning on a data set containing 797 gamma-ray bursts taken from
the BATSE 3B catalog. Assuming all attributes to be distributed logNormally,
Mukherjee et al. (1998) analyzed these same data using a statistical cluster
analysis. Utilizing the logarithmic values for T90 duration, total fluence, and
hardness ratio HR321 their results showed the instances formed three classes.
Class I contained long/bright/intermediate bursts, class II consisted of
short/faint/hard bursts and class III was represented by
intermediate/intermediate/soft bursts.
  When ESX was presented with these data and restricted to forming a small
number of classes, the two classes found by previous standard techniques were
determined. However, when ESX was allowed to form more than two classes, four
classes were created. One of the four classes contained a majority of short
bursts, a second class consisted of mostly intermediate bursts, and the final
two classes were subsets of the Class I (long) bursts determined by Mukherjee
et al. We hypothesize that systematic biases may be responsible for this
variation.
