A Novel index-based multidimensional data organization model that enhances the predictability of the machine learning algorithms

TL;DR

This paper introduces a new index-based data organization model for multidimensional data that improves the efficiency of data access and enhances the predictability of machine learning algorithms.

Contribution

The paper proposes a novel ordered index-based data organization model that reduces complexity and improves learning performance in multidimensional datasets.

Findings

Enhanced predictability for supervised learning algorithms.

Improved data access efficiency through ordered data organization.

Effective mapping of multidimensional data in reduced space.

Abstract

Learning from the multidimensional data has been an interesting concept in the field of machine learning. However, such learning can be difficult, complex, expensive because of expensive data processing, manipulations as the number of dimension increases. As a result, we have introduced an ordered index-based data organization model as the ordered data set provides easy and efficient access than the unordered one and finally, such organization can improve the learning. The ordering maps the multidimensional dataset in the reduced space and ensures that the information associated with the learning can be retrieved back and forth efficiently. We have found that such multidimensional data storage can enhance the predictability for both the unsupervised and supervised machine learning algorithms.