 ethodologically, proponents of a lightweight method are
duty-bound to describe an operational assessment criteria. Our
criteria is based on treatments: a good association selects a portion
of the test data that contains something we desire or something we
with to avoid. Without such a criteria, it is hard to assess
lightweight vs heavyweight approaches. In our view, such a criteria
must be {\em algorithmic}, {\em semantic} and {\em portable}. Reports
of the effectiveness of an algorithm such as learner $L$ could find
associations within 10,000,000 examples using only
843MB~\cite{agrawal94} are exciting. However, the  {\em algorithmic}
nature of this report does not assess any {\em semantic} issues. If
the learnt associations weren't ``useful'', however that is defined,
then that learning effort was wasted. ``Usefulness'' can be assessed
via domain experts who understand the semantics of a domain. Such a
measure is not {\em portable}:  researchers across the globe rarely
have access to the same domain experts.

The advantage of treatment-based assessment is that  they are a
portable semantic assessment criteria. That is, other researchers
could demonstrate the ``usefulness''  of their learner on the
examples shown in this paper, without
 having to (e.g.) interview domain experts. The
disadvantage of treatment-based assessment is that it is not a
widely-accepted criteria. Other papers in the fields of association
rule learner do not express the efficacy of their associations in
terms of the treatments found in their associations. Hence, this
paper can only offer {\em baseline measurements}, not comparative
measurements,  of a new sub-class of learner; i.e. a treatment
association rule learner.

Analogous results exist in the machine learning (ML) community. For
example, Holte found that C4.5 performs only moderately better than
1R (1R is a learner which only generates decision trees with a
maximum depth of one)~\cite{holte93}



Note that Holte's results are consistent with ML data sets he
explored contained narrow funnels. 1-level deep decision trees work
since that is enough to check for assignments to the funnels
variables.


Lightweight approaches may not necessarily replace heavier methods.
For example,  the {\em CBR-heavy} community rejects {\em CBR-lite},
arguing that {\em CBR-lite} tends to dodge the most interesting and
fundamental problems. Menzies \& Cukic caution that {\em
testing-lite} is inappropriate for mission-critical and
safety-critical systems. Similarly, Holte does not propose 1R as a
replacement for C4.5; rather, he used it to argue that most of the
datasets used to test learners does not contain very complex
relationships. When ML-lite methods such as 1R miss such complex
relationships, heavier weight methods are required.

On the other hand, if most domains lack such complex relationships,
then ML-lite is an attractive option. One advantage of exploring
simpler methods before complex ones is that ML-lite algorithms can
generate very simple theories. Such simpler theories are easier to
understand.
