With application in fast data mining

Reducing Computational complexity is a major issue in data mining. Mapping to Multidimensional Optimal Regions (M
²
OR) is a special purposed method for multiclass classification task. It reduces computational complexity in comparison to the other concepts of classifiers. In this paper, the accuracy of M
²
OR increases using Learning Inductive Riemannian Manifold in Abstract from (LIRMA). LIRMA estimates the underlying structure of a dataset with respect to the embedded dynamical system of data. The estimated non-linear mapping of LIRMA has the advantage of being topology preserving and inductivity. As a result, the optimal regions are determined more precisely. Consequently, the accuracy of M
²
OR increases and the memory complexity decreases accordingly.