Finding Highly Correlated Pairs Efficiently with Powerful Pruning

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CitationIn Proc. of the 2006 ACM Conference on Information and Knowledge Management
AuthorsJian Zhang
Joan Feigenbaum


We consider the problem of finding highly correlated pairs in a large data set. That is, given a threshold not too small, we wish to report all the pairs of items (or binary attributes) whose (Pearson) correlation coefficients are greater than the threshold. Correlation analysis is an important step in many statistical and knowledge-discovery tasks. Normally, the number of highly correlated pairs is quite small compared to the total number of pairs. Identifying highly correlated pairs in a naive way by computing the correlation coefficients for all the pairs is wasteful. With massive data sets, where the total number of pairs may exceed the main-memory capacity, the computational cost of the naive method is prohibitive. In their KDD'04 paper [15], Hui Xiong et al. address this problem by proposing the TAPER algorithm. The algorithm goes through the data set in two passes. It uses the first pass to generate a set of candidate pairs whose correlation coefficients are then computed directly in the second pass. The efficiency of the algorithm depends greatly on the selectivity (pruning power) of its candidate-generating stage. In this work, we adopt the general framework of the TAPER algorithm but propose a different candidate-generation method. For a pair of items, TAPER's candidate-generation method considers only the frequencies (supports) of individual items. Our method also considers the frequency (support) of the pair but does not explicitly count this frequency (support). We give a simple randomized algorithm whose false-negative probability is negligible. The space and time complexities of generating the candidate set in our algorithm are asymptotically the same as TAPER's. We conduct experiments on synthesized and real data. The results show that our algorithm produces a greatly reduced candidate set -- one that can be several orders of magnitude smaller than that generated by TAPER. Because of this, our algorithm uses much less memory and can be faster. The former is critical for dealing with massive data.

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