TAPER: A two-step approach for all-strong-pairs correlation query in large databases

Hui Xiong, Shashi Shekhar, Pang Ning Tan, Vipin Kumar

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Given a user-specified minimum correlation threshold θ 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 θ. However, when the number of items and transactions are large, the computation cost of this query can be very high. The goal of this paper is to provide computationally efficient algorithms to answer the all-strong-pairs correlation query. Indeed, 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 special monotone properties which allow pruning of many item pairs even without computing their upper bounds. A Two-step All-strong-Pairs corElation queRy (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 of or improves when the number of items is increased in data sets with Zipf-like or linear rank-support distributions. Experimental results from synthetic and real-world data sets exhibit similar trends and show that the TAPER algorithm can be an order of magnitude faster than brute-force alternatives. Finally, we demonstrate that the algorithmic ideas developed in the TAPER algorithm can be extended to efficiently compute negative correlation and uncentered Pearson's correlation coefficient.

Original languageEnglish (US)
Pages (from-to)493-508
Number of pages16
JournalIEEE Transactions on Knowledge and Data Engineering
Volume18
Issue number4
DOIs
StatePublished - Apr 2006

Bibliographical note

Funding Information:
This work was partially supported by US National Science Foundation (NSF) grant IIS-0308264, NSF grant ITR ACI-0325949, and by the US Army High Performance Computing Research Center under the auspices of the Department of the Army, Army Research Laboratory cooperative agreement number DAAD19-01-2-0014. The content of this work does not necessarily reflect the position or policy of the government and no official endorsement should be inferred. Access to computing facilities was provided by the AHPCRC and the Minnesota Supercomputing Institute. In addition, the authors thank Professor Joydeep Ghosh (University of Texas, Austin) and Professor Ke Wang (Simon Fraser University, Canada) for valuable comments. Finally, they are grateful to Kim Koffolt for her timely and detailed feedback to help improve the readability of this paper.

Keywords

  • Association analysis
  • Data mining
  • Pearson's correlation coefficient
  • Statistical computing

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