  This paper introduces a new data analysis method for big data using a newly
defined regression model named multiple model linear regression(MMLR), which
separates input datasets into subsets and construct local linear regression
models of them. The proposed data analysis method is shown to be more efficient
and flexible than other regression based methods. This paper also proposes an
approximate algorithm to construct MMLR models based on
$(\epsilon,\delta)$-estimator, and gives mathematical proofs of the correctness
and efficiency of MMLR algorithm, of which the time complexity is linear with
respect to the size of input datasets. This paper also empirically implements
the method on both synthetic and real-world datasets, the algorithm shows to
have comparable performance to existing regression methods in many cases, while
it takes almost the shortest time to provide a high prediction accuracy.

Machine Learning;Databases