Correlation analysis among variables are frequently used in various approaches of statistics and data mining. However, its application to the data obtained from the recent ubiquitous sensing system consisting of massive sensors is often intractable, since its computational complexity is proportional to the square number of variables. On the other hand, the strong correlations among the variables are usually sparse in various data such as small world data. In this report, we propose a novel method to efficiently estimate the correlations among massive variables under this sparseness. Its experimental evaluations show excellent performance in efficiency comparing with the direct computation of all correlation coefficients.
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