Efficiently Mining Long Patterns from Databases
Roberto J. Bayardo Jr. (IBM Almaden Research Center)
We present a pattern-mining algorithm that scales roughly linearly in
the number of maximal patterns embedded in a database irrespective of
the length of the longest pattern. In comparison, previous algorithms
based on Apriori scale exponentially with longest pattern
length. Experiments on real data show that when the patterns are long,
our algorithm is more efficient by an order of magnitude or more.