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Multi-assignment clustering for boolean data

Authors:

Andreas P. Streich,

Joachim M. BuhmannAuthors Info & Claims

The Journal of Machine Learning Research, Volume 13

Pages 459 - 489

Published: 01 February 2012 Publication History

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Abstract

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.

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Index Terms

Multi-assignment clustering for boolean data
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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cover image The Journal of Machine Learning Research

The Journal of Machine Learning Research Volume 13, Issue

3/1/2012

2065 pages

ISSN:1532-4435

EISSN:1533-7928

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JMLR.org

Publication History

Published: 01 February 2012

Published in JMLR Volume 13

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Cited By

Lu HChen XShi JVaidya JAtluri VHong YHuang W(2020)Algorithms and Applications to Weighted Rank-one Binary Matrix FactorizationACM Transactions on Management Information Systems10.1145/338659911:2(1-33)Online publication date: 3-May-2020
https://dl.acm.org/doi/10.1145/3386599
Chang YMa HSu YLi Z(2020)An Overlapping Community Detection with Subspaces on Double-ViewsNeural Information Processing10.1007/978-3-030-63833-7_14(164-176)Online publication date: 18-Nov-2020
https://dl.acm.org/doi/10.1007/978-3-030-63833-7_14
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https://dl.acm.org/doi/10.1109/TIP.2019.2897940
Saenko IKotenko ITakadama K(2018)Genetic algorithms for role mining in critical infrastructure data spacesProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3208283(1688-1695)Online publication date: 6-Jul-2018
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Saenko IKotenko I(2018)Genetic Algorithms for Solving Problems of Access Control Design and Reconfiguration in Computer NetworksACM Transactions on Internet Technology10.1145/309389818:3(1-21)Online publication date: 6-Mar-2018
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He TChan K(2018)Discovering Fuzzy Structural Patterns for Graph AnalyticsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2018.279195126:5(2785-2796)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1109/TFUZZ.2018.2791951
Liu CHsaio WChang TJou T(2017)Nonparametric multi-assignment clusteringIntelligent Data Analysis10.3233/IDA-16010521:4(893-911)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.3233/IDA-160105
Saenko IKotenko I(2017)Administrating role-based access control by genetic algorithmsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3082509(1463-1470)Online publication date: 15-Jul-2017
https://dl.acm.org/doi/10.1145/3067695.3082509
Alonso JParedes RRosso P(2017)Feature representation for social circles detection using MACNeural Computing and Applications10.1007/s00521-016-2222-y28:9(2395-2402)Online publication date: 1-Sep-2017
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