A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Spectral partitioning works: planar graphs and finite element meshes
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Iterative Projected Clustering by Subspace Mining
IEEE Transactions on Knowledge and Data Engineering
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
CLAN: An Algorithm for Mining Closed Cliques from Large Dense Graph Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Coherent closed quasi-clique discovery from large dense graph databases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A spectral clustering approach to optimally combining numericalvectors with a modular network
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Effective Pruning Techniques for Mining Quasi-Cliques
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
ACM Transactions on Knowledge Discovery from Data (TKDD)
Closed patterns meet n-ary relations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Discovering Relevant Cross-Graph Cliques in Dynamic Networks
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Managing and Mining Graph Data
Managing and Mining Graph Data
Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
DB-CSC: a density-based approach for subspace clustering in graphs with feature vectors
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
External evaluation measures for subspace clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
Community mining from multi-relational networks
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Interesting Multi-relational Patterns
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Community Detection with Edge Content in Social Media Networks
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Community discovery and profiling with social messages
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining coherent subgraphs in multi-layer graphs with edge labels
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Finding density-based subspace clusters in graphs with feature vectors
Data Mining and Knowledge Discovery
Substructure clustering: a novel mining paradigm for arbitrary data types
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
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Detecting dense subgraphs in a large graph is an important graph mining problem and various approaches have been proposed for its solution. While most existing methods only consider unlabeled and one-dimensional graph data, many real-world applications provide far richer information. Thus, in our work, we consider graphs that contain different types of edges -- represented as different layers/dimensions of a graph -- as well as edge labels that further characterize the relations between two vertices. We argue that exploiting this additional information supports the detection of more interesting clusters. In general, we aim at detecting clusters of vertices that are densely connected by edges with similar labels in subsets of the graph layers. So far, there exists only a single method that tries to detect clusters in such graphs. This method, however, is highly sensitive to noise: already a single edge with a deviating label can completely hinder the detection of interesting clusters. In this paper, we present the RCS (Robust Coherent Subgraph) model which enables us to detect clusters even in noisy data. This robustness greatly enhances the applicability on real-world data. In order to obtain interpretable results, RCS avoids redundant clusters in the result set. We present the algorithm RMiCS for an efficient detection of RCS clusters and we analyze its behavior in various experiments on synthetic and real-world data.