Authors : Ahmed Burhan Mohammed and Ahmad Abdullah Mohammed AL-Mafrji
Abstract: The sensors scattered around the world and the precision devices that work on the internet have formed the so-called internet of things. As these devices expose large amounts of information to the central data storage for decision. Taking the right decision in real-time requires analyzing these data for the purpose of getting the right thing done. In order to make the right decisions on people and things using data mining techniques and machine learning algorithms helps make decisions. Internet of things that inject large amounts of data needs to be studied, analyzed and disseminated in order to access valuable, useful and bug-free information for the purpose of making the right decision and avoiding problems. In this study, presents two clustering algorithm simple k-means and Self Organizing Map (SOM) in industrial data used IoT devices. Next, comparing the clustering models of 2 algorithms output in IoT dataset that improved the SOM is better than k-means but it is slower in creating the model.
Ahmed Burhan Mohammed and Ahmad Abdullah Mohammed AL-Mafrji, 2018. Comparative Analysis of Machine Learning Techniques, Clustering Algorithms Impact in Internet of Things. Journal of Engineering and Applied Sciences, 13: 10802-10806.