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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)
Detecting community structure is crucial for uncovering the links between structures and functions in complex networks. Most of contemporary community detection algorithms employ single optimization criteria (e.g., modularity), which may have fundamental disadvantages. This paper considers the community detection process as a Multi-Objective optimization(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)
—A longitudinal social network evolves over time through the creation and/or deletion of links among a set of actors (e.g. individuals or organisations). Longitudinal social networks are studied by network science and social science researchers to understand network evolution, trend propagation, friendship and belief formation, diffusion of innovations, the(More)