K-mean Clustering in Web Service Quality Datasets Using AWS and RapidMiner

Today, number of research works are under process using computing resources delivered as a service over the network. This paper focuses in the field of cloud computing and proposes the model for service clustering of cab services using k-means clustering. This paper proposes a highly competent and efficient methodology. Our research includes the web service development using AWS, real time data collection and its analysis based on clustering. The web services are designed to provide approximate time and distance as per client inputs and then produces the output in terms of cost of different cab services provided by the cab service providers. Through our clustering approach, we are proposing the better approach for selecting the best service using data analysis of web service quality dataset over AWS based developed model.