Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
When Is ''Nearest Neighbor'' Meaningful?
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Tractable Group Detection on Large Link Data Sets
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A simple solution to the k-core problem
Random Structures & Algorithms - Proceedings from the 12th International Conference “Random Structures and Algorithms”, August1-5, 2005, Poznan, Poland
Community detection in large-scale social networks
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
EDSC: efficient density-based subspace clustering
Proceedings of the 17th ACM conference on Information and knowledge management
ACM Transactions on Knowledge Discovery from Data (TKDD)
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Managing and Mining Graph Data
Managing and Mining Graph Data
Clustering Large Attributed Graphs: An Efficient Incremental Approach
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms
ICDM '10 Proceedings of the 2010 IEEE International Conference on 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
Tracing clusters in evolving graphs with node attributes
Proceedings of the 21st ACM international conference on Information and knowledge management
Projective clustering ensembles
Data Mining and Knowledge Discovery
Density-based Community Identification and Visualisation
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Combining Relations and Text in Scientific Network Clustering
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Efficient community detection in large networks using content and links
Proceedings of the 22nd international conference on World Wide Web
Detecting and exploring clusters in attributed graphs: a plugin for the gephi platform
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Data sources representing attribute information in combination with network information are widely available in today's applications. To realize the full potential for knowledge extraction, mining techniques like clustering should consider both information types simultaneously. Recent clustering approaches combine subspace clustering with dense subgraph mining to identify groups of objects that are similar in subsets of their attributes as well as densely connected within the network. While those approaches successfully circumvent the problem of full-space clustering, their limited cluster definitions are restricted to clusters of certain shapes. In this work, we introduce a density-based cluster definition taking the attribute similarity in subspaces and the graph density into account. This novel cluster model enables us to detect clusters of arbitrary shape and size. We avoid redundancy in the result by selecting only the most interesting non-redundant clusters. Based on this model, we introduce the clustering algorithm DB-CSC. In thorough experiments we demonstrate the strength of DB-CSC in comparison to related approaches.