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IRMAOC: an interpretable role mining algorithm based on overlapping clustering

CYBERSECURITY 2025年第1期8卷 1-18页

作者： Yang, Yaqi Li, Jun'e Zhang, Tao Chen, Lu Huang, Guirong Lv, Zhuo Wuhan Univ Sch Cyber Sci & Engn Key Lab Aerosp Informat Secur & Trusted Comp Minist Educ Wuhan Peoples R China State Grid Smart Grid Res Inst Co Ltd Nanjing 210003 Peoples R China State Grid Lab Power Cyber Secur Protect & Monitor Nanjing 210003 Peoples R China State Grid Henan Elect Power Res Inst Zhengzhou 450052 Peoples R China

The Industrial Internet motivates the research and development of Zero-Trust Architecture (ZTA). Role-Based Access Control (RBAC) as one of the key technologies for ZTA has become a hot topic. Role mining algorithms are crucial for RBAC and interpretable role mining receives wide attention due to its virtue of mining meaningful roles. However, the roles generated by existing algorithms have low interpretability and high time complexity, limiting their application in practice. This paper proposes an Interpretable Role Mining Algorithm Based on overlapping clustering (IRMAOC). It evaluates the interpretability of a role based on user similarity calculated on the permission and attribute of the role, and employs policy interpretability as the metric of a role set. IRMAOC creates a user association graph and clusters to generate candidate roles based on the graph. Then it remains the roles whose interpretability is higher than the preset threshold and re-clusters the users belonging to the other roles until the interpretability of all roles are higher than the threshold. Experimental results show that our algorithm significantly improves the interpretability of roles, reduces the Weighted Structure Complexity (WSC), and decreases time complexity compared to previous works.

关键词： Role-based access control Interpretability of roles Role mining overlapping clustering

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Blockchain-based secure data sharing with overlapping clustering and searchable encryption

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COMPUTER STANDARDS & INTERFACES 2025年 93卷

作者： Xu, Shujiang Dong, Shishi Wang, Lianhai Mihaljevie, Miodrag J. Zhang, Shuhui Shao, Wei Wang, Qizheng Qilu Univ Technol Shandong Acad Sci Shandong Comp Sci Ctr Key Lab Comp Power Network & Informat SecurMinist Jinan 250014 Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Ind Network & Informat Syst Jinan 250014 Peoples R China Serbid Acad Sci & Arts Math Inst Belgrade 11000 Serbia

In the digital age, the importance of data sharing has significantly increased as it accelerates the release of value from data elements. Nevertheless, data confronts substantial security threats, including potential leakage during outsourcing and sharing procedures. To ensure the security of shared data, plaintext data is often replaced by encrypted data for sharing, and searchable encryption algorithms are used to improve the efficiency of sharing. However, due to the inherent limitations of searchable encryption schemes, existing secure data-sharing approaches frequently encounter inefficient search capabilities and privacy violations. This paper proposes a secure data-sharing scheme for encrypted data that integrates blockchain with an overlapping clustering technique to tackle these challenges. The proposed scheme combines blockchain with an attribute-based searchable encryption method to guarantee data transparency, trustworthiness, and confidentiality. Furthermore, the scheme significantly enhances search efficiency and accuracy by incorporating overlapping clustering and keyword relevance-based ranking strategies. Experimental results show that this scheme effectively enhances search efficiency and provides robust privacy protection.

关键词： Blockchain Access control Searchable encryption overlapping clustering

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overlapping clustering of gene expression data using penalized weighted normalized cut

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GENETIC EPIDEMIOLOGY 2018年第8期42卷 796-811页

作者： Hidalgo, Sebastian J. Teran Zhu, Tingyu Wu, Mengyun Ma, Shuangge Yale Univ Dept Biostat New Haven CT 06520 USA Xiamen Univ Dept Stat Xiamen Peoples R China Shanghai Univ Finance & Econ Sch Stat & Management Shanghai 200433 Peoples R China

clustering has been widely conducted in the analysis of gene expression data. For complex diseases, it has played an important role in identifying unknown functions of genes, serving as the basis of other analysis, and others. A common limitation of most existing clustering approaches is to assume that genes are separated into disjoint clusters. As genes often have multiple functions and thus can belong to more than one functional cluster, the disjoint clustering results can be unsatisfactory. In addition, due to the small sample sizes of genetic profiling studies and other factors, there may not be sufficient evidence to confirm the specific functions of some genes and cluster them definitively into disjoint clusters. In this study, we develop an effective overlapping clustering approach, which takes account into the multiplicity of gene functions and lack of certainty in practical analysis. A penalized weighted normalized cut (PWNCut) criterion is proposed based on the NCut technique and an L2 norm constraint. It outperforms multiple competitors in simulation. The analysis of the cancer genome atlas (TCGA) data on breast cancer and cervical cancer leads to biologically sensible findings which differ from those using the alternatives. To facilitate implementation, we develop the function pwncut in the R package NCutYX.

关键词： gene expression data NCut overlapping clustering penalization

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overlapping clustering: A Review

Overlapping Clustering: A Review

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SAI Computing Conference (SAI)

作者： Baadel, Said Thabtah, Fadi Lu, Joan Canadian Univ Dubai U Arab Emirates Univ Huddersfield Huddersfield W Yorkshire England Nelson Marlborough Inst Technol Auckland New Zealand

ISBN: (纸本)9781467384605

Data clustering or unsupervised classification is one of the main research area in Data Mining. Partitioning clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard ( crisp) partitioning techniques where each object is assigned to one cluster. Other algorithms utilise overlapping techniques where an object may belong to one or more clusters. Partitioning algorithms that overlap include the commonly used Fuzzy K-means and its variations. Other more recent algorithms reviewed in this paper are: the overlapping K-Means (OKM), Weighted OKM (WOKM), the overlapping Partitioning Cluster (OPC), and the Multi-Cluster overlapping K-means Extension (MCOKE). This review focuses on the above mentioned partitioning methods and future direction in overlapping clustering is highlighted in this paper.

关键词： Data mining clustering k-means MCOKE overlapping clustering

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OClustR: A new graph-based algorithm for overlapping clustering

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NEUROCOMPUTING 2013年 121卷 234-247页

作者： Perez-Suarez, Airel Martinez-Trinidad, Jose F. Carrasco-Ochoa, Jesus A. Medina-Pagola, Jose E. Inst Nacl Astrofis Opt & Electr Puebla 72840 Mexico Ctr Aplicac Tecnol Avanzada Havana 12200 Cuba

clustering is a Data Mining technique, which has been widely used in many practical applications. From these applications, there are some, like social network analysis, topic detection and tracking, information retrieval, categorization of digital libraries, among others, where objects may belong to more than one cluster;however, most clustering algorithms build disjoint clusters. In this work, we introduce OClustR, a new graph-based clustering algorithm for building overlapping clusters. The proposed algorithm introduces a new graph-covering strategy and a new filtering strategy, which together allow to build overlapping clusterings more accurately than those built by previous algorithms. The experimental evaluation, conducted over several standard collections, showed that our proposed algorithm builds less clusters than those built by the previous related algorithms. Additionally, OClustR builds clusters with overlapping closer to the real overlapping in the collections than the overlapping generated by other clustering algorithms. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Data mining overlapping clustering Graph-based algorithms

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Categorization of Multiple Documents Using Fuzzy overlapping clustering Based on Formal Concept Analysis

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2020年第5期30卷 631-647页

作者： Chen, Yi-Hui Lu, Eric Jui-Lin Cheng, Ya-Wen Chang Gung Univ Dept Informat Management Taoyuan 33302 Taiwan Kaohsiung Chang Gung Mem Hosp Kawasaki Dis Ctr Kaohsiung Taiwan Natl Chung Hsing Univ Dept Management Informat Syst Taichung Taiwan

Most clustering algorithms build disjoint clusters. However, clusters might be overlapped because documents may belong to two or more categories in the real world. For example, a paper discussing the Apple Watch may be categorized into either 3C, Fashion, or even Clothing and Shoes. Therefore, overlapping clustering algorithms have been studied such that a resource can be assigned to one or more clusters. Formal Concept Analysis (FCA), which has many practical applications in information science, has been used in disjoin clustering, but has not been studied in overlapping clustering. To make overlapping clustering possible by using FCA, we propose an approach, including two types of transformation. From the experimental results, it shows that the proposed fuzzy overlapping clustering performed more efficiently than existing overlapping clustering methods. The positive results confirm the feasibility of the proposed scheme used in overlapping clustering. Also, it can be used in applications such as recommendation systems.

关键词： overlapping clustering Formal Concept Analysis fuzzy logic

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Non-Exhaustive, overlapping clustering

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2019年第11期41卷 2644-2659页

作者： Whang, Joyce Jiyoung Hou, Yangyang Gleich, David F. Dhillon, Inderjit S. Sungkyunkwan Univ SKKU Dept Comp Sci & Engn Suwon 440746 South Korea Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Univ Texas Austin Dept Comp Sci Austin TX 78712 USA

Traditional clustering algorithms, such as K-Means, output a clustering that is disjoint and exhaustive, i.e., every single data point is assigned to exactly one cluster. However, in many real-world datasets, clusters can overlap and there are often outliers that do not belong to any cluster. While this is a well-recognized problem, most existing algorithms address either overlap or outlier detection and do not tackle the problem in a unified way. In this paper, we propose an intuitive objective function, which we call the NEO-K-Means (Non-Exhaustive, overlapping K-Means) objective, that captures the issues of overlap and non-exhaustiveness in a unified manner. Our objective function can be viewed as a reformulation of the traditional K-Means objective, with easy-to-understand parameters that capture the degrees of overlap and non-exhaustiveness. By considering an extension to weighted kernel K-Means, we show that we can also apply our NEO-K-Means idea to overlapping community detection, which is an important task in network analysis. To optimize the NEO-K-Means objective, we develop not only fast iterative algorithms but also more sophisticated algorithms using low-rank semidefinite programming techniques. Our experimental results show that the new objective and algorithms are effective in finding ground-truth clusterings that have varied overlap and non-exhaustiveness;for the case of graphs, we show that our method outperforms state-of-the-art overlapping community detection algorithms.

关键词： clustering algorithms Linear programming Kernel Iterative methods Computer science Anomaly detection Task analysis overlapping clustering K-Means outlier semidefinite programming graph clustering community detection

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A tree-based incremental overlapping clustering method using the three-way decision theory

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KNOWLEDGE-BASED SYSTEMS 2016年 91卷 189-203页

作者： Yu, Hong Zhang, Cong Wang, Guoyin Chongqing Univ Posts & Telecommun Chongqing Key Lab Computat Intelligence Chongqing 400065 Peoples R China

Existing clustering approaches are usually restricted to crisp clustering, where objects just belong to one cluster;meanwhile there are some applications where objects could belong to more than one cluster. In addition, existing clustering approaches usually analyze static datasets in which objects are kept unchanged after being processed;however many practical datasets are dynamically modified which means some previously learned patterns have to be updated accordingly. In this paper, we propose a new tree-based incremental overlapping clustering method using the three-way decision theory. The tree is constructed from representative points introduced by this paper, which can enhance the relevance of the search result. The overlapping cluster is represented by the three-way decision with interval sets, and the three-way decision strategies are designed to updating the clustering when the data increases. Furthermore, the proposed method can determine the number of clusters during the processing. The experimental results show that it can identifies clusters of arbitrary shapes and does not sacrifice the computing time, and more results of comparison experiments show that the performance of proposed method is better than the compared algorithms in most of cases. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Incremental clustering overlapping clustering Search tree Three-way decision theory

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ADAPTIVE ESTIMATION IN STRUCTURED FACTOR MODELS WITH APPLICATIONS TO overlapping clustering

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ANNALS OF STATISTICS 2020年第4期48卷 2055-2081页

作者： Bing, Xin Bunea, Florentina Ning, Yang Wegkamp, Marten Cornell Univ Dept Stat & Data Sci Ithaca NY 14853 USA Cornell Univ Dept Math White Hall Ithaca NY 14853 USA

This work introduces a novel estimation method, called LOVE, of the entries and structure of a loading matrix A in a latent factor model X = AZ + E, for an observable random vector X is an element of R-p, with correlated unobservable factors Z is an element of R-K, with K unknown, and uncorrelated noise E. Each row of A is scaled, and allowed to be sparse. In order to identify the loading matrix A, we require the existence of pure variables, which are components of X that are associated, via A, with one and only one latent factor. Despite the fact that the number of factors K, the number of the pure variables and their location are all unknown, we only require a mild condition on the covariance matrix of Z, and a minimum of only two pure variables per latent factor to show that A is uniquely defined, up to signed permutations. Our proofs for model identifiability are constructive, and lead to our novel estimation method of the number of factors and of the set of pure variables, from a sample of size n of observations on X. This is the first step of our LOVE algorithm, which is optimization-free, and has low computational complexity of order p(2). The second step of LOVE is an easily implementable linear program that estimates A. We prove that the resulting estimator is near minimax rate optimal for A, with respect to the parallel to parallel to(infinity)(,q) loss, for q >= 1, up to logarithmic factors in p, and that it can be minimax-rate optimal in many cases of interest. The model structure is motivated by the problem of overlapping variable clustering, ubiquitous in data science. We define the population level clusters as groups of those components of X that are associated, via the matrix A, with the same unobservable latent factor, and multifactor association is allowed. Clusters are respectively anchored by the pure variables, and form overlapping subgroups of the p-dimensional random vector X. The Latent model approach to overlapping clustering is reflected in

关键词： overlapping clustering latent model identification high-dimensional estimation minimax estimation pure variables group recovery support recovery sparse loading matrix matrix factorization adaptive estimation

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Partition-based overlapping clustering Using Cluster's Parameters and Relations 9

Partition-based Overlapping Clustering Using Cluster's Param...

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9th International Conference on Knowledge and Smart Technology (KST)

作者： Limungkura, Tanawat Vateekul, Peerapon Chulalongkorn Univ Dept Comp Engn Bangkok Thailand

ISBN: (纸本)9781467390774

Conventional clustering algorithms based on the assumption that a data point can be assigned to only a single cluster. In spite of, there are several types of data that a data point belongs to multiple categories and causes ground-truth clusters overlap. To handle this situation, several algorithms are proposed and referred as "overlapping clustering". One of state-of-the-art partition-based overlapping clustering technique is "Non-exhaustive, overlapping K-Means" or "NEO-K-Means" in short, which is an extension of K-Means clustering algorithm. Although NEO-K-Means works effectively for most real-world multi-category data. However, the process of assigning clusters focuses only on the minimum distance from the data to the centroid and ignores other essential parameters, such as, distance between clusters and the radius of clusters. Moreover, the number of estimation of data points on overlapping area is also still not accurate enough. These are huge draw backs of NEO-K-Means that makes clustering accuracy lower than it should be. In this paper, we aim to overcome this limitation in NEO-K-Means by using radius of cluster and distance between clusters for assisting in estimating data and assigning clusters. The experimental results show that our method significantly outperforms NEO-K-Means on nine real multi-category data sets in terms of F-1.

关键词： clustering overlapping clustering Mufti-Label Data Mufti-Category Data K-Means

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