A Novel Index-based Multidimensional Data Organization Model that Enhances the Predictability of the Machine Learning Algorithms


Mahbubur Rahman, North American University, USA


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.


Multidimensional, Euclidean norm, cosine similarity, database, model, hash table, index, Knearest neighbour, K-means clustering.

Full Text  Volume 10, Number 12