Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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
A cross-collection mixture model for comparative text mining
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)
Cross-relational clustering with user's guidance
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Relational clustering for multi-type entity resolution
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Multi-way distributional clustering via pairwise interactions
ICML '05 Proceedings of the 22nd international conference on Machine learning
Introducing Game Theory and its Applications
Introducing Game Theory and its Applications
Spectral clustering for multi-type relational data
ICML '06 Proceedings of the 23rd international conference on Machine learning
Unsupervised learning on k-partite graphs
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
LinkClus: efficient clustering via heterogeneous semantic links
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Mining Maximal Quasi-Bicliques to Co-Cluster Stocks and Financial Ratios for Value Investment
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Clicks: An effective algorithm for mining subspace clusters in categorical datasets
Data & Knowledge Engineering
IEEE Transactions on Knowledge and Data Engineering
Computers and Operations Research
An effective algorithm for mining 3-clusters in vertically partitioned data
Proceedings of the 17th ACM conference on Information and knowledge management
RankClus: integrating clustering with ranking for heterogeneous information network analysis
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A comparison of extrinsic clustering evaluation metrics based on formal constraints
Information Retrieval
Ranking-based clustering of heterogeneous information networks with star network schema
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Simple search methods for finding a Nash equilibrium
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Towards fault-tolerant formal concept analysis
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
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Heterogeneous information networks are pervasive in applications ranging from bioinformatics to e-commerce. As a result, unsupervised learning and clustering methods pertaining to such networks have gained significant attention recently. Nodes in a heterogeneous information network are regarded as objects derived from distinct domains such as 'authors' and 'papers'. In many cases, feature sets characterizing the objects are not available, hence, clustering of the objects depends solely on the links and relationships amongst objects. Although several previous studies have addressed information network clustering, shortcomings remain. First, the definition of what constitutes an information network cluster varies drastically from study to study. Second, previous algorithms have generally focused on non-overlapping clusters, while many algorithms are also limited to specific network topologies. In this paper we introduce a game theoretic framework (GHIN) for defining and mining clusters in heterogeneous information networks. The clustering problem is modeled as a game wherein each domain represents a player and clusters are defined as the Nash equilibrium points of the game. Adopting the abstraction of Nash equilibrium points as clusters allows for flexible definition of reward functions that characterize clusters without any modification to the underlying algorithm. We prove that well-established definitions of clusters in 2-domain information networks such as formal concepts, maximal bi-cliques, and noisy binary tiles can always be represented as Nash equilibrium points. Moreover, experimental results employing a variety of reward functions and several real world information networks illustrate that the GHIN framework produces more accurate and informative clusters than the recently proposed NetClus and state of the art MDC algorithms.