exploiting a support-based upper bound of pearson's correlation coefficient for efficiently identifying strongly correlated pairs
Given a user-specified minimum correlation threshold ? and a market basket database with N items and T transactions, an all-strong-pairs correlation query finds all item pairs with correlations above the threshold ?. However, when the number of items and transactions are large, the computation cost of this query can be very high. In this paper, we identify an upper bound of Pearson's correlation coefficient for binary variables. This upper bound is not only much cheaper to compute than Pearson's correlation coefficient but also exhibits a special monotone property which allows pruning of many item pairs even without computing their upper bounds. A T wo-step A ll-strong- P airs corr E lation que Ry (TAPER) algorithm is proposed to exploit these properties in a filter-and-refine manner. Furthermore, we provide an algebraic cost model which shows that the computation savings from pruning is independent or improves when the number of items is increased in data sets with common Zipf or linear rank-support distributions. Experimental results from synthetic and real data sets exhibit similar trends and show that the TAPER algorithm can be an order of magnitude faster than brute-force alternatives.
pearson's correlation coefficient statistical computing