一种新的基于索引的多维数据组织模型，增强了机器学习算法的可预测性
A Novel index-based multidimensional data organization model that enhances the predictability of the machine learning algorithms
摘要：
从多维数据中学习一直是机器学习领域中一个有趣的概念。然而，这种学习可能是困难的，复杂的，昂贵的，因为昂贵的数据处理，随着维数的增加操纵。因此，我们引入了一个有序的基于索引的数据组织模型，因为有序的数据集比无序的数据集提供了更容易和有效的访问，最后，这种组织可以提高学习。排序将多维数据集映射到缩减的空间中，并确保可以有效地来回检索与学习相关的信息。我们已经发现，这种多维数据存储可以增强无监督和监督机器学习算法的可预测性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
