Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
The minimization of spatially-multiplexed character sets
Communications of the ACM
Introduction to algorithms
Proposed NIST standard for role-based access control
ACM Transactions on Information and System Security (TISSEC)
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Role mining - revealing business roles for security administration using data mining technology
Proceedings of the eighth ACM symposium on Access control models and technologies
Noisy-OR Component Analysis and its Application to Link Analysis
The Journal of Machine Learning Research
The role mining problem: finding a minimal descriptive set of roles
Proceedings of the 12th ACM symposium on Access control models and technologies
Fast exact and heuristic methods for role minimization problems
Proceedings of the 13th ACM symposium on Access control models and technologies
Mining roles with semantic meanings
Proceedings of the 13th ACM symposium on Access control models and technologies
A class of probabilistic models for role engineering
Proceedings of the 15th ACM conference on Computer and communications security
Multi-assignment clustering for Boolean data
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Variational probabilistic inference and the QMR-DT network
Journal of Artificial Intelligence Research
Discovery of optimal factors in binary data via a novel method of matrix decomposition
Journal of Computer and System Sciences
On the definition of role mining
Proceedings of the 15th ACM symposium on Access control models and technologies
The Indian Buffet Process: An Introduction and Review
The Journal of Machine Learning Research
The minimum transfer cost principle for model-order selection
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Role Mining with Probabilistic Models
ACM Transactions on Information and System Security (TISSEC)
Discovering social circles in ego networks
ACM Transactions on Knowledge Discovery from Data (TKDD) - Casin special issue
Characteristic matrix of covering and its application to Boolean matrix decomposition
Information Sciences: an International Journal
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We propose a probabilistic model for clustering Boolean data where an object can be simultaneously assigned to multiple clusters. By explicitly modeling the underlying generative process that combines the individual source emissions, highly structured data are expressed with substantially fewer clusters compared to single-assignment clustering. As a consequence, such a model provides robust parameter estimators even when the number of samples is low. We extend the model with different noise processes and demonstrate that maximum-likelihood estimation with multiple assignments consistently infers source parameters more accurately than single-assignment clustering. Our model is primarily motivated by the task of role mining for role-based access control, where users of a system are assigned one or more roles. In experiments with real-world access-control data, our model exhibits better generalization performance than state-of-the-art approaches.