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
Proposed NIST standard for role-based access control
ACM Transactions on Information and System Security (TISSEC)
Role mining - revealing business roles for security administration using data mining technology
Proceedings of the eighth ACM symposium on Access control models and technologies
Stability-based validation of clustering solutions
Neural Computation
The role mining problem: finding a minimal descriptive set of roles
Proceedings of the 12th ACM symposium on Access control models and technologies
A cost-driven approach to role engineering
Proceedings of the 2008 ACM symposium on Applied computing
Fast exact and heuristic methods for role minimization problems
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
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A probabilistic approach to hybrid role mining
Proceedings of the 16th ACM conference on Computer and communications security
On the definition of role mining
Proceedings of the 15th ACM symposium on Access control models and technologies
Proceedings of the 15th ACM symposium on Access control models and technologies
On quality of monitoring for multi-channel wireless infrastructure networks
Proceedings of the eleventh ACM international symposium on Mobile ad hoc networking and computing
Scalable discovery of best clusters on large graphs
Proceedings of the VLDB Endowment
Adversaries' Holy Grail: access control analytics
Proceedings of the First Workshop on Building Analysis Datasets and Gathering Experience Returns for Security
Model order selection for boolean matrix factorization
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Overlapping clusters for distributed computation
Proceedings of the fifth ACM international conference on Web search and data mining
Multi-assignment clustering for boolean data
The Journal of Machine Learning Research
Advantage of overlapping clusters for minimizing conductance
LATIN'12 Proceedings of the 10th Latin American international conference on Theoretical Informatics
Proceedings of the 17th ACM symposium on Access Control Models and Technologies
Role Mining with Probabilistic Models
ACM Transactions on Information and System Security (TISSEC)
Ensuring continuous compliance through reconciling policy with usage
Proceedings of the 18th ACM symposium on Access control models and technologies
An optimization framework for role mining
Journal of Computer Security
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Conventional clustering methods typically assume that each data item belongs to a single cluster. This assumption does not hold in general. In order to overcome this limitation, we propose a generative method for clustering vectorial data, where each object can be assigned to multiple clusters. Using a deterministic annealing scheme, our method decomposes the observed data into the contributions of individual clusters and infers their parameters. Experiments on synthetic Boolean data show that our method achieves higher accuracy in the source parameter estimation and superior cluster stability compared to state-of-the-art approaches. We also apply our method to an important problem in computer security known as role mining. Experiments on real-world access control data show performance gains in generalization to new employees against other multi-assignment methods. In challenging situations with high noise levels, our approach maintains its good performance, while alternative state-of-the-art techniques lack robustness.