Fibonacci heaps and their uses in improved network optimization algorithms
Journal of the ACM (JACM)
Fast and effective text mining using linear-time document clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
Mining Graph Data
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Clustering graphs for visualization via node similarities
Journal of Visual Languages and Computing
Graph Node Clustering via Transitive Node Similarity
PCI '10 Proceedings of the 2010 14th Panhellenic Conference on Informatics
Scalable discovery of best clusters on large graphs
Proceedings of the VLDB Endowment
Clustering Large Attributed Graphs: A Balance between Structural and Attribute Similarities
ACM Transactions on Knowledge Discovery from Data (TKDD)
Graph-based clustering with constraints
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
Improving the accuracy of similarity measures by using link information
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
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Assorted networks have transpired for analysis and visualization, including social community network, biological network, sensor network and many other information networks. Prior approaches either focus on the topological structure or attribute likeness for graph clustering. A few recent methods constituting both aspects however cannot be scalable with elevated time complexity. In this paper, we have developed an intra-graph clustering strategy using collaborative similarity measure (IGC-CSM) which is comparatively scalable to medium scale graphs. In this approach, first the relationship intensity among vertices is calculated and then forms the clusters using k-Medoid framework. Empirical analysis is based on density and entropy, which depicts the efficiency of IGC-CSM algorithm without compromising on the quality of the clusters.