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Cluster analysis is one of attractive data mining technique that use in many fields. One popular class of data clustering algorithms is the center based clustering algorithm. K-means used as a popular clustering method due to its simplicity and high speed in clustering large datasets. However, K-means has two shortcomings: dependency on the initial state(More)
This study is dedicated to proposing a two-stage method, which first uses Self-Organizing Feature Maps (SOM) neural network to determine the number of clusters and cluster centroids, then uses honey bee mating optimization algorithm based on K-means algorithm to find the final solution. The results of simulated data via a Monte Carlo study show that the(More)
One of the challenging problems when studying complex networks is the detection of sub-structures, called communities. Network communities emerge as dense parts, while they may have a few relationships to each other. Indeed, communities are latent among a mass of nodes and edges in a sparse network. This characteristic makes the community detection process(More)
  • Mohammad Julashokri, Mohammad Fathian, Mohammad Reza Gholamian, Ahmad Mehrbod
  • 2011
By the expanse of internet stores and products, recommender systems have emerged to increase store attractiveness and develop online customers. Recommender systems are systems which help customers to find product that they want. These systems recommend product to individual customer according to their preferences and interests. Recommender systems use(More)
A novel approach for role mining in the context of role engineering for role-based access control is developed in this paper. We propose a simple algorithm, based on the assumption that permissions from the same role appear near each other in the access history log. Closely co-occurring groups of permissions are selected as candidate roles and are ranked(More)