Building Machine Learning Algorithms on Hadoop for Bigdata
Machine Learning (ML) is at the core of data analysis. Machine Learning Algorithms (MLA) are sequential and recursive and the accuracy of MLA's rely on size of the data (i.e., greater the data more accurate is the result). Absence of a reliable framework for MLA to work for big data has made these algorithms to cripple their ability to reach the fullest potential. Hadoop is one such framework that offers distributed storage and parallel data processing. The existing problem to implement MLA on Hadoop is that the MLA's need data to be stored in single place because of its recursive nature, but Hadoop does not support data sharing.