Rich AT EnglishLogicKernel DOT com
JS>: In any case, data mining is a good example of
1. Data mining starts with a database of
2. It applies well-defined algorithm(s) that
DB to discover patterns in
Although the interesting algorithms aren't
that well defined - they're evolving like stem cells and continue to this day.
3. Those patterns might be the result of
or they might be accidental
patterns that could be
violated by the next update
to the database.
Or they might be bound to the
conceptualizations in the observer's cranium, whether fundamental or preaproved
or officially not.
4. Some additional analysis and testing is
to distinguish principles
Recursion does that very nicely. If
you can learn it once, you can learn it N times.
Points #3 and #4 are critical. Data mining is
a method for discovering patterns, but it has no
for distinguishing patterns that result from
principles from accidental patterns that result from
mere coincidence. That is the weakness of
And the strength of data mining.