On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
LA-WEB '03 Proceedings of the First Conference on Latin American Web Congress
Local clustering of large graphs by approximate fiedler vectors
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
Distributed graph clustering for application in wireless networks
IWSOS'11 Proceedings of the 5th international conference on Self-organizing systems
On the NP-Completeness of some graph cluster measures
SOFSEM'06 Proceedings of the 32nd conference on Current Trends in Theory and Practice of Computer Science
Local clustering of large graphs by approximate fiedler vectors
WEA'05 Proceedings of the 4th international conference on Experimental and Efficient Algorithms
Topic mining based on graph local clustering
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
A separability framework for analyzing community structure
ACM Transactions on Knowledge Discovery from Data (TKDD) - Casin special issue
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Most graph-theoretical clustering algorithms require the complete adjacency relation of the graph representing the examined data. This is infeasible for very large graphs currently emerging in many application areas. We propose a local approach that computes clusters in graphs, one at a time, relying only on the neighborhoods of the vertices included in the current cluster candidate. This enables implementing a local and parameter-free algorithm. Approximate clusters may be identified quickly by heuristic methods. We report experimental results on clustering graphs using simulated annealing.