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1. 发明申请

US20120246098A1 Role Mining With User Attribution Using Generative Models 有权
标题翻译：使用生成模型的用户归因的角色挖掘
公开(公告)号：US20120246098A1
公开(公告)日：2012-09-27
申请号：US13411174
申请日：2012-03-02
申请人： Suresh N. Chari , Ian Michael Molloy , Youngja Park
发明人： Suresh N. Chari , Ian Michael Molloy , Youngja Park
IPC分类号： G06F15/18
CPC分类号： G06N99/005 , G06F21/604
摘要： Applications of machine learning techniques such as Latent Dirichlet Allocation (LDA) and author-topic models (ATM) to the problems of mining of user roles to specify access control policies from entitlement as well as logs which contain record of the usage of these entitlements are provided. In one aspect, a method for performing role mining given a plurality of users and a plurality of permissions is provided. The method includes the following steps. At least one generative machine learning technique, e.g., LDA, is used to obtain a probability distribution θ for user-to-role assignments and a probability distribution β for role-to-permission assignments. The probability distribution θ for user-to-role assignments and the probability distribution β for role-to-permission assignments are used to produce a final set of roles, including user-to-role assignments and role-to-permission assignments.
摘要翻译：潜在的Dirichlet分配（LDA）和作者主题模型（ATM）等机器学习技术的应用对于用户角色的挖掘问题，从授权中指定访问控制策略以及包含这些权利使用记录的日志的应用是提供。在一个方面，提供了赋予多个用户和多个权限的用于执行角色挖掘的方法。该方法包括以下步骤。使用至少一种生成机器学习技术，例如LDA来获得概率分布; 用于角色角色分配和概率分布＆bgr; 用于角色到权限分配。概率分布与概念; 用于角色角色分配和概率分布; 角色到权限分配用于生成一组最终角色，包括用户角色分配和角色到权限分配。

2. 发明申请

US20130097103A1 Techniques for Generating Balanced and Class-Independent Training Data From Unlabeled Data Set 审中-公开
标题翻译：从非标准数据集中生成平衡和类别独立训练数据的技术
公开(公告)号：US20130097103A1
公开(公告)日：2013-04-18
申请号：US13274002
申请日：2011-10-14
申请人： Suresh N. Chari , Ian Michael Molloy , Youngja Park , Zijie Qi
发明人： Suresh N. Chari , Ian Michael Molloy , Youngja Park , Zijie Qi
IPC分类号： G06F15/18 , G06F17/30
CPC分类号： G06N20/00
摘要： Techniques for creating training sets for predictive modeling are provided. In one aspect, a method for generating training data from an unlabeled data set is provided which includes the following steps. A small initial set of data is selected from the unlabeled data set. Labels are acquired for the initial set of data selected from the unlabeled data set resulting in labeled data. The data in the unlabeled data set is clustered using a semi-supervised clustering process along with the labeled data to produce data clusters. Data samples are chosen from each of the clusters to use as the training data. The selecting, presenting, clustering and choosing steps are repeated with one or more additional sets of data selected from the unlabeled data set until a desired amount of training data has been obtained, wherein at each iteration an amount of the labeled data is increased.
摘要翻译：提供了用于创建预测建模训练集的技术。一方面，提供了一种用于从未标记的数据集生成训练数据的方法，包括以下步骤。从未标记的数据集中选择一小段初始数据。从未标记的数据集中选择的初始数据集中获取标签，从而产生标记数据。未标记数据集中的数据使用半监督聚类过程与标记数据一起聚类以产生数据集群。从每个群集中选择数据样本以用作训练数据。使用从未标记的数据集中选择的一个或多个附加数据集重复选择，呈现，聚类和选择步骤，直到获得了所需量的训练数据，其中在每次迭代时，标记数据的量增加。

3. 发明授权

US08983877B2 Role mining with user attribution using generative models 有权
标题翻译：使用生成模型的角色挖掘与用户归因
公开(公告)号：US08983877B2
公开(公告)日：2015-03-17
申请号：US13411174
申请日：2012-03-02
申请人： Suresh N. Chari , Ian Michael Molloy , Youngja Park
发明人： Suresh N. Chari , Ian Michael Molloy , Youngja Park
IPC分类号： G06N5/00 , G06F1/00 , G06N99/00 , G06F21/60
CPC分类号： G06N99/005 , G06F21/604
摘要： Applications of machine learning techniques such as Latent Dirichlet Allocation (LDA) and author-topic models (ATM) to the problems of mining of user roles to specify access control policies from entitlement as well as logs which contain record of the usage of these entitlements are provided. In one aspect, a method for performing role mining given a plurality of users and a plurality of permissions is provided. The method includes the following steps. At least one generative machine learning technique, e.g., LDA, is used to obtain a probability distribution θ for user-to-role assignments and a probability distribution β for role-to-permission assignments. The probability distribution θ for user-to-role assignments and the probability distribution β for role-to-permission assignments are used to produce a final set of roles, including user-to-role assignments and role-to-permission assignments.
摘要翻译：潜在的Dirichlet分配（LDA）和作者主题模型（ATM）等机器学习技术的应用对于用户角色的挖掘问题，从授权中指定访问控制策略以及包含这些权利使用记录的日志的应用是提供。在一个方面，提供了赋予多个用户和多个权限的用于执行角色挖掘的方法。该方法包括以下步骤。使用至少一种生成机器学习技术，例如LDA来获得概率分布; 用于角色角色分配和概率分布＆bgr; 用于角色到权限分配。概率分布与概念; 用于角色角色分配和概率分布; 角色到权限分配用于生成一组最终角色，包括用户角色分配和角色到权限分配。

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