Prediction Data Processing Scheme using an Artificial Neural Network and Data Clustering for Big Data

Se-Hoon Jung (Sunchon National University)
Jong-Chan Kim (Sunchon National University)
Chun-Bo Sim (Sunchon National University)

International Journal of Electrical and Computer Engineering
Vol 6, No 1: February 2016

Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster choice through principal component analysis, and expanding the scope of data clustering. The technique also applies an artificial neural network learning model for user recommendation and prediction from the clustered data. The proposed processing model for predicted data generated results that improved the existing artificial neural network–based data clustering and learning model by approximately 9.25%.