  Given a user-specified minimum correlation threshold and a transaction
database, the problem of mining all-strong correlated pairs is to find all item
pairs with Pearson's correlation coefficients above the threshold . Despite the
use of upper bound based pruning technique in the Taper algorithm [1], when the
number of items and transactions are very large, candidate pair generation and
test is still costly. To avoid the costly test of a large number of candidate
pairs, in this paper, we propose an efficient algorithm, called Tcp, based on
the well-known FP-tree data structure, for mining the complete set of
all-strong correlated item pairs. Our experimental results on both synthetic
and real world datasets show that, Tcp's performance is significantly better
than that of the previously developed Taper algorithm over practical ranges of
correlation threshold specifications.
