earticle

영어

Advances in digital sensors, communications, computation, and storage have created huge collections of data, capturing information of value to business, science, government, and society. Many machine learning algorithms do not scale beyond data sets of a few million elements or cannot tolerate the statistical noise and gaps found in real-world data. Further research is required to develop algorithms that apply in real-world situations and on data sets of trillions of elements. In this paper we propose a conjugate based novel algorithm to handle the huge collections of data. Through the experimental results, proposed method performs well on huge data from UCI machine learning repository data set.