k-means algorithm

Cloud computing provides computational services such as servers, memory, storage space, databases, networks, software, analytics, and information as virtualized resources through the internet to offer faster innovation, flexible resources, and cost savings at scale. Although cloud computing service providers are innovatively expanding their services, trust is one of the major obstacles to the progress of this matter. Trust is the biggest issue in cloud computing since trust is an effective guarantor during interactions between the users and the providers. Trust is one of the most fundamental methods for increasing confidence in resources provided in the cloud environment and is important in cloud business environments. With the increasing number of cloud services providers in the cloud computing environment and the number of users, the selection of provider has become a major challenge. The Coa algorithm has a higher convergence speed, at least by 5.9%, compared to the studied algorithms. In this research, an optimization approach based on a metaheuristic process using the COA algorithm combined with the K-means clustering algorithm is proposed to solve the optimization problem of selecting the best provider in the trust management third-party component layer based on parameters. In this method, while reducing trust evaluation time, the accuracy of user access to the most trusted provider based on user priorities has increased compared to previous methods. This can increase user confidence and improve the quality of service providers.

Nowadays, clustering plays an important role in most research fields such as engineering, medicine, biology, data mining, etc. In fact, clustering means unsupervised division. By using it, the data are divided into categories that are more similar to each other in terms of the parameters of interest. One of the famous methods in this field is k-means. In this method, despite the dependence on initial conditions and convergence to local optimal points, N numbers of data are grouped into k clusters with high speed. In this article, to solve the existing problems, the combined method is used based on evolutionary algorithms, chaos theory and k-means; that is in addition to solving the mentioned problems, it will also be independent of the number of variables. In this article, for the purpose of validation, the proposed methods are implemented on 13 different famous collections, and the results are compared with genetic algorithm, particle community, bee colony, simulated refrigeration, differential evolution, harmony search, and k-means methods. The high ability and robustness of these methods will be evident based on the results.