Abstract: Aspects of the subject disclosure may include, for example, systems and methods for generating structured datasets for predicting bond price. The systems and methods include constructing a price function of each bond contained in a plurality of bond clusters including a target cluster, training a machine learning model to determine a cause for an erroneous price prediction result, and generating structured datasets based on a feedback from the machine learning model. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, systems and methods for generating structured datasets for predicting bond price. The systems and methods include constructing a price function of each bond contained in a plurality of bond clusters including a target cluster, training a machine learning model to determine a cause for an erroneous price prediction result, and generating structured datasets based on a feedback from the machine learning model. Other embodiments are disclosed.

Abstract: A method and system for determining clustering relevance in a volatile data environment and adjusting clustering composition for improved accuracy are disclosed. The method includes plotting a dataset and generating at least one grand truth data value, and clustering the plotted dataset for generating data clusters, in which the clustering is performed based on correlation of individual data values included in the dataset. The method further includes independently training machine learning (ML) algorithm for each of the data clusters for generating a managing ML algorithm for the dataset, applying the managing ML algorithm to the dataset for predicting at least one future data value, and comparing differences between the grand truth data value and the future data value for estimating a clustering error, and adjusting composition of at least one of the plurality of data clusters based on the estimated clustering error.