Algorithms for clustering data
Algorithms for clustering data
A near optimal algorithm for edge separators (preliminary version)
STOC '94 Proceedings of the twenty-sixth annual ACM symposium on Theory of computing
Eigenvalues, flows and separators of graphs
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
ACM Computing Surveys (CSUR)
A clustering algorithm based on graph connectivity
Information Processing Letters
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Between Min Cut and Graph Bisection
MFCS '93 Proceedings of the 18th International Symposium on Mathematical Foundations of Computer Science
Cluster Graph Modification Problems
WG '02 Revised Papers from the 28th International Workshop on Graph-Theoretic Concepts in Computer Science
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Spectral partitioning works: planar graphs and finite element meshes
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Documents clustering using tolerance rough set model and its application to information retrieval
Intelligent exploration of the web
Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters
IEEE Transactions on Computers
A measure for cluster cohesion in semantic overlay networks
Proceedings of the 2008 ACM workshop on Large-Scale distributed systems for information retrieval
Orca Reduction and ContrAction Graph Clustering
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
Clustering dense graphs: A web site graph paradigm
Information Processing and Management: an International Journal
Graph-based clustering for computational linguistics: a survey
TextGraphs-5 Proceedings of the 2010 Workshop on Graph-based Methods for Natural Language Processing
Evaluating the quality of clustering algorithms using cluster path lengths
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Multilevel local search algorithms for modularity clustering
Journal of Experimental Algorithmics (JEA)
Density-constrained graph clustering
WADS'11 Proceedings of the 12th international conference on Algorithms and data structures
Is there a best quality metric for graph clusters?
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Advantage of overlapping clusters for minimizing conductance
LATIN'12 Proceedings of the 10th Latin American international conference on Theoretical Informatics
Fuzzy Sets and Systems
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Assessing the quality of multilevel graph clustering
Data Mining and Knowledge Discovery
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A promising approach to graph clustering is based on the intuitive notion of intracluster density versus intercluster sparsity. As for the weighted case, clusters should accumulate lots of weight, in contrast to their connection to the remaining graph, which should be light. While both formalizations and algorithms focusing on particular aspects of this rather vague concept have been proposed, no conclusive argument on their appropriateness has been given. In order to deepen the understanding of particular concepts, including both quality assessment as well as designing new algorithms, we conducted an experimental evaluation of graph-clustering approaches. By combining proved techniques from graph partitioning and geometric clustering, we also introduce a new approach that compares favorably.