Title: Extreme learning machine and K-means clustering for the improvement of link prediction in social networks using analytic hierarchy process
Authors: Gowri Thangam Jeyaraj; A. Sankar
Addresses: Department of Computer Applications, PSG College of Technology, Coimbatore, Tamil Nadu, 641004, India ' Department of Computer Applications, PSG College of Technology, Coimbatore, Tamil Nadu, 641004, India
Abstract: The rapid growth of the availability of healthcare related data raises a challenge of extracting useful information. Thus there is an urgent need for the healthcare industry to predict the disease, that reduces the amount of cumbersome tests on patients The aim of this paper is to employ a combination of machine learning algorithms namely extreme learning machine algorithm with k-means clustering and analytic hierarchy process, for the prediction of disease in a patient through the extraction of different patterns from the dataset based on the relationships that exists among the attributes. It would help the physician and the medical scientists to predict the possibility of the disease. In today's era, the percentage of females getting affected by diabetes has increased exponentially. So, the experiments are carried over PIMA diabetes data set that focuses on females are extracted from UCI repository and the results are found to be significant.
Keywords: analytic hierarchy process; extreme learning machine; K-means clustering; social networks; link prediction; network management.
International Journal of Enterprise Network Management, 2019 Vol.10 No.3/4, pp.371 - 388
Received: 23 Nov 2018
Accepted: 24 Mar 2019
Published online: 17 Oct 2019 *