Elements of information theory
Elements of information theory
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Multilevel k-way partitioning scheme for irregular graphs
Journal of Parallel and Distributed Computing
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Fully automatic cross-associations
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for ontology-driven subspace clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Multi-way distributional clustering via pairwise interactions
ICML '05 Proceedings of the 22nd international conference on Machine learning
Parameter-Free Spatial Data Mining Using MDL
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Relevance search and anomaly detection in bipartite graphs
ACM SIGKDD Explorations Newsletter
Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Center-piece subgraphs: problem definition and fast solutions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data
IEEE Transactions on Visualization and Computer Graphics
MatrixExplorer: a Dual-Representation System to Explore Social Networks
IEEE Transactions on Visualization and Computer Graphics
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
IBM Journal of Research and Development
A compression-boosting transform for two-dimensional data
AAIM'06 Proceedings of the Second international conference on Algorithmic Aspects in Information and Management
Fast detection of size-constrained communities in large networks
WISE'10 Proceedings of the 11th international conference on Web information systems engineering
An MDL approach to efficiently discover communities in bipartite network
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Hierarchical clustering and outlier detection for effective image data organization
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
DEMON: a local-first discovery method for overlapping communities
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Optimal Spatial Resolution for the Analysis of Human Mobility
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
On community detection in real-world networks and the importance of degree assortativity
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Given a large bipartite graph (like document-term, or userproduct graph), how can we find meaningful communities, quickly, and automatically? We propose to look for community hierarchies, with communities- within-communities. Our proposed method, the Context-specific Cluster Tree (CCT)finds such communities at multiple levels, with no user intervention, based on information theoretic principles (MDL). More specifically, it partitions the graph into progressively more refined subgraphs, allowing users to quickly navigate from the global, coarse structure of a graph to more focused and local patterns. As a fringe benefit, and also as an additional indication of its quality, it also achieves better compression than typical, non-hierarchical methods. We demonstrate its scalability and effectiveness on real, large graphs.