چكيده به لاتين
Common clustering methods, regardless of the content of the node and only relying on the Structure graph, clustering doing. However, the use of content nodes during of structural clustering can boost the meaningfulness of clusters. In fact, the purpose of graph clustering problem is detection clusters with coherent internal structure and homogeneous attribute values. It is quite challenging because of similarities in structure and content, independent look or even at cross-purposes and must balance between them. Moreover, in most clustering methods available is taken into account only one aspect of the structure or content. However, recently a number of methods proposed, which clustering structure and content as they do. However, given that the usual method of clustering, clustering based on the structure of the graph are doing most of the existing evaluation measure are structural measure. For this reason, existing evaluation methods using these measures, can not accurately and desirable to evaluate the structural-content clustering methods.
In this thesis, a clustering method based propagation label named SC-Cluster is proposed. This method by balancing the similarity of structure and content, to properly perform clustering. In addition, the clustering method for more accurate evaluation of structural-content clustering, SC-ErrorLink measure is proposed. This measure, by considering both the structure and content of the evaluation does. The results show that the proposed clustering method with balance between structural and content aspects clusters, detects clusters of more favorable.
