Information Sciences最新文献

Fast and direct co-clustering via adaptive anchor graph learning 通过自适应锚图学习快速直接的共聚类

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-28 DOI: 10.1016/j.ins.2025.122349

Jiaqi Nie , Qianyao Qiang

{"title":"Fast and direct co-clustering via adaptive anchor graph learning","authors":"Jiaqi Nie , Qianyao Qiang","doi":"10.1016/j.ins.2025.122349","DOIUrl":"10.1016/j.ins.2025.122349","url":null,"abstract":"<div><div>Spectral clustering faces three main challenges: high computational costs arising from regular graph construction and eigenvalue decomposition, important information loss and solution deviation due to the ‘relaxation and re-discretization’ strategy, and the inflexibility of fixed graphs that fail to adapt to unreliable similarities. To address these issues, Fast and Direct Co-Clustering via adaptive anchor graph learning (FDC<sup>2</sup>) is proposed. This method dynamically learns the anchor graph and efficiently solves the co-clustering problem. FDC<sup>2</sup> iteratively refines the anchor graph using raw data, anchors, and an evolving indicator matrix, thus reducing computational complexity while enabling the modification of unreliable similarities. The learned anchor similarity matrix is used as the weight matrix in a bipartite graph, facilitating the simultaneous co-clustering of raw data and anchors. To directly and efficiently solve the discrete indicator matrix, a coordinate descent method is employed. Extensive experimental results demonstrate that FDC<sup>2</sup> significantly reduces computational time and delivers superior clustering results.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122349"},"PeriodicalIF":8.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generalized multiplicative interval type-2 fuzzy partition C-means clustering 广义乘法区间2型模糊划分c均值聚类

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-28 DOI: 10.1016/j.ins.2025.122356

Chengmao Wu, Yulong Gao

{"title":"Generalized multiplicative interval type-2 fuzzy partition C-means clustering","authors":"Chengmao Wu, Yulong Gao","doi":"10.1016/j.ins.2025.122356","DOIUrl":"10.1016/j.ins.2025.122356","url":null,"abstract":"<div><div>This paper enhances generalized multiplicative fuzzy sets through a membership value fuzzification technique and introduces generalized multiplicative type-2 fuzzy sets. We simplify the complexity of these sets to create generalized multiplicative interval type-2 fuzzy sets and outline their operations. Building on this foundation, we propose a novel generalized multiplicative interval type-2 fuzzy partition and present a generalized multiplicative interval type-2 fuzzy C-means clustering model with dual fuzzy weighting exponents. Additionally, we introduce a type-reduction method for generalized multiplicative interval type-2 fuzzy sets, leading to a new two-level alternative iteration algorithm for clustering. Experimental results show that our algorithm improves clustering performance, outperforming the generalized multiplicative fuzzy partition clustering algorithm. Performance metrics, including Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI), indicate improvements of 1 % to 4 % for numerical data and 1 % to 11 % for image data. Comparisons with existing type-2 fuzzy clustering algorithms show improvements of 1 % to 5 % for numerical data and 3 % to 17 % for image data.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"718 ","pages":"Article 122356"},"PeriodicalIF":8.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Tobit Kalman filtering for random parameter 2-D system: Dealing with amplify-and-forward relay and stochastic communication protocol 随机参数二维系统的分布式Tobit卡尔曼滤波：处理放大转发中继和随机通信协议

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-28 DOI: 10.1016/j.ins.2025.122355

Yuanyuan Li, Jinling Liang

{"title":"Distributed Tobit Kalman filtering for random parameter 2-D system: Dealing with amplify-and-forward relay and stochastic communication protocol","authors":"Yuanyuan Li, Jinling Liang","doi":"10.1016/j.ins.2025.122355","DOIUrl":"10.1016/j.ins.2025.122355","url":null,"abstract":"<div><div>This article is devoted to dealing with the distributed Tobit Kalman filtering issue for a class of random parameter two-dimensional systems over sensor networks. To enhance the signal quality after long-distance transmission, an amplify-and-forward (AF) relay node is deployed between each sensor and its corresponding remote filter. Additionally, the stochastic communication protocol (SCP) is utilized to regulate the data transmission order, which could effectively mitigate the potential network congestion and overload. Firstly, considering the presence of measurement censoring, the conditional expectation and variance of the censored measurement regarding the system state are calculated under AF relay and SCP framework. Subsequently, the distributed Tobit Kalman filter (TKF) is established, where each local filter could acquire data from its own node and the neighboring ones under a known network topology. Finally, upper bound of the filtering error variance is derived and further locally minimized in the trace sense by adopting the mathematical induction method and some matrix analysis techniques. The simulation results are also provided to show validity of the proposed distributed TKF.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"718 ","pages":"Article 122355"},"PeriodicalIF":8.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Order, subset construction and sequential pattern mining 顺序、子集构造和顺序模式挖掘

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-27 DOI: 10.1016/j.ins.2025.122348

Slimane Oulad-Naoui , Hadda Cherroun , Djelloul Ziadi

{"title":"Order, subset construction and sequential pattern mining","authors":"Slimane Oulad-Naoui , Hadda Cherroun , Djelloul Ziadi","doi":"10.1016/j.ins.2025.122348","DOIUrl":"10.1016/j.ins.2025.122348","url":null,"abstract":"<div><div>Sequential Pattern Mining (SPM) is a basic task in data mining. It aims to extract the most occurring sequences in a dataset, which turns out to be instrumental in many fields. In <span><span>[1]</span></span> we initiated an attempt to formally unify leading pattern mining approaches. This paper builds upon our previous work to first extend the polynomial model to SPM. Next, we devise an efficient implementation termed WASMA that enhances the standard subset construction method. To do so, we first partition the set of states into independent sets based on their labels, and then define three different state ordering. The first is a global id-based order which we use in global exploration. The second is local and used in itemset extension. A geometric ordering is lastly exploited to avoid redundant computations. To handle the memory bottleneck of the determinization, we propose two variants: WASMA-wsc and WASMA-ssc that rely or not on the state existence check clause. Unlike existing approaches that overlook the appearance of repetitive computation paths, the first variant introduces a novel feature, since it avoids recomputing previously explored sub-branches of the problem space. Besides, we refine for the SPM setting the well-known theoretical upper-bound by establishing new complexities in function of the geometric-order topology. Evaluations demonstrate that our solution outperforms existing approaches for SPM instances with very low support thresholds, persisting sole to yield the result while its competitors hit the time limit.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122348"},"PeriodicalIF":8.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OCCM-RPS: Ordered credal C-means clustering based on random permutation set OCCM-RPS：基于随机排列集的有序凭证c均值聚类

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-27 DOI: 10.1016/j.ins.2025.122329

Luyuan Chen , Pierpaolo D'Urso , Yong Deng

{"title":"OCCM-RPS: Ordered credal C-means clustering based on random permutation set","authors":"Luyuan Chen , Pierpaolo D'Urso , Yong Deng","doi":"10.1016/j.ins.2025.122329","DOIUrl":"10.1016/j.ins.2025.122329","url":null,"abstract":"<div><div>Evidential clustering has received extensive attention due to its ability to generate credal partitions that represent cluster-membership uncertainty. However, credal partitions based on mass functions can not reflect the propensity information between samples and clusters, and existing evidential clustering methods rarely incorporate attribute weights into distance functions. To address these two shortcomings, we introduce the Random Permutation Set (RPS) into evidential clustering frameworks for the first time, and propose a novel approach called Ordered Credal C-means clustering based on RPS (OCCM-RPS). Specifically, we first present an improved coefficient of variant to determine attribute weights in a simple and effective manner. Secondly, we introduced a new concept of ordered credal partition to depict clustering results, which can both quantitatively represent the cluster-membership uncertainty and qualitatively reflect the propensity of samples toward different clusters. Twelve well-known benchmark datasets and two synthetic datasets are employed to evaluate the effectiveness of OCCM-RPS, and experimental results show that the proposed OCCM-RPS can capture original characteristics of datasets more comprehensively using a higher-order form of data representation, and significantly improve the hard clustering performance compared with the state-of-the-art evidential clustering algorithms.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122329"},"PeriodicalIF":8.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards heterogeneous federated graph learning via structural entropy and prototype aggregation 基于结构熵和原型聚合的异构联邦图学习

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-27 DOI: 10.1016/j.ins.2025.122338

Zhehao Dai , Guojiang Shen , Haopeng Yuan , Shangfei Zheng , Yuyue Hu , Jiaxin Du , Xiangjie Kong , Feng Xia

{"title":"Towards heterogeneous federated graph learning via structural entropy and prototype aggregation","authors":"Zhehao Dai , Guojiang Shen , Haopeng Yuan , Shangfei Zheng , Yuyue Hu , Jiaxin Du , Xiangjie Kong , Feng Xia","doi":"10.1016/j.ins.2025.122338","DOIUrl":"10.1016/j.ins.2025.122338","url":null,"abstract":"<div><div>In today's data-driven landscape, Federated Graph Learning (FGL) facilitates collaborative training between distributed data while providing robust privacy protections. However, FGL faces significant challenges in practical application: data heterogeneity owing to divergent node distributions and graph structures across clients, coupled with model heterogeneity caused by heterogeneous GNN architectures, substantially impedes the aggregation efficacy and generalization capabilities of global models. Existing FGL frameworks often overlook the unique impact of graph topology, inherent to graph data, between data and model heterogeneity. We propose an innovative framework called Structural Entropy Federated Graph Learning (SEFGL) that leverages structural entropy to simultaneously address data and model heterogeneity. At the client level, structural entropy-based virtual node generation and graph reconstruction techniques are applied to strengthen minority class node representations and optimize local graph topology while maintaining the original data distribution. At the server level, a prototype learning approach based on structural entropy aggregates data from clients with similar entropy characteristics. This enables each client to acquire a more diverse global representation, fostering the development of a personalized and robust prototype. Experiments conducted on three graph datasets demonstrate that the SEFGL framework achieves superior performance in terms of generalizability, efficiency, and effectiveness in high-heterogeneity scenarios.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"718 ","pages":"Article 122338"},"PeriodicalIF":8.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel virtual sample generation method based on dual correlation generative adversarial nets for soft sensing application 基于对偶相关生成对抗网络的虚拟样本生成方法在软测量中的应用

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-26 DOI: 10.1016/j.ins.2025.122334

Qun-Xiong Zhu , Jie-Long Zhang , Xiao-Lu Song , Yan-Lin He , Yuan Xu

{"title":"A novel virtual sample generation method based on dual correlation generative adversarial nets for soft sensing application","authors":"Qun-Xiong Zhu , Jie-Long Zhang , Xiao-Lu Song , Yan-Lin He , Yuan Xu","doi":"10.1016/j.ins.2025.122334","DOIUrl":"10.1016/j.ins.2025.122334","url":null,"abstract":"<div><div>In industrial production processes, due to the limitation of the environment and economy, the soft sensor models faced the challenge of poor prediction effect caused by the small sample. To solve the above challenges, this paper puts forward a novel virtual sample generation method based on dual correlation generative adversarial nets (DCRGAN-VSG). In DCRGAN, Wasserstein GAN (WGAN) is used to generate the process variables, the improved Conditional GAN (CGAN) is used to generate the quality variables, and the mean square error is added to the value function of CGAN to improve the mapping relationship between the process variables and quality variables. In addition, the Pielou index is used to select process variables that can fill the sparse region from the generated samples. Finally, training soft sensors using the dataset containing virtual samples. The benchmark function and industrial case called the purified terephthalic acid (PTA) data are used to verify the virtual sample generation performance of the DCRGAN-VSG. Simulation results show that DCRGAN-VSG improves the accuracy of soft sensing models by approximately 70% in benchmark experiments and by nearly 40% in industrial case studies.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122334"},"PeriodicalIF":8.1,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient multi-party privacy preserving federated k-means based on homomorphic encryption 基于同态加密的高效多方隐私保护联合k-均值

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-26 DOI: 10.1016/j.ins.2025.122335

Zeng-Ao Tang , Xue-Feng Duan , Rong-Hua Liang , Yong Ding

{"title":"Efficient multi-party privacy preserving federated k-means based on homomorphic encryption","authors":"Zeng-Ao Tang , Xue-Feng Duan , Rong-Hua Liang , Yong Ding","doi":"10.1016/j.ins.2025.122335","DOIUrl":"10.1016/j.ins.2025.122335","url":null,"abstract":"<div><div>Nowadays, the collection and storage of data is increasingly decentralized, and the demand for data mining on distributed data is growing. Traditional k-means risks privacy leaks through direct data sharing. Existing privacy-preserving methods still expose intermediate cluster details during iterations. This paper introduces DTK-means (distributed privacy-preserving k-means) clustering algorithm, a distributed privacy-preserving k-means method addressing these issues. It involves multiple users and two non-colluding servers. The user locally computes centroids, encrypts them, and submits to the server. The two servers then aggregate these encrypted centroids into global cluster centroids with multiplicative perturbation, ensuring that neither the participants nor the servers have knowledge of the specific details. The scheme includes four algorithms for compute local centroids, data aggregation, compute global centroids and output final cluster centroids, implemented using Paillier homomorphic encryption. An extensive performance analysis is carried out to show that DTK-means ensures that intermediate data and private data are concealed from all parties involved. Participants can accurately perform k-means clustering using these hidden global centroids without any information loss. Furthermore, it can resist collusion attacks, even if one server colludes with all participants except one. Complexity analysis and numerical experiments show that our algorithm has good efficiency.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122335"},"PeriodicalIF":8.1,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A rate allocation model for VVC intercoding using a quality dependency 基于质量依赖的VVC互编码的速率分配模型

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-26 DOI: 10.1016/j.ins.2025.122333

Heqiang Wang , Xuekai Wei , Mingliang Zhou , Horace Ho Shing Ip , Sam Kwong

引用次数: 0

Supply network disruption: A framework for assessing vulnerability and implementing resilience strategies 供应网络中断：评估脆弱性和实施弹性战略的框架

IF 8.1 1区计算机科学

Information Sciences Pub Date : 2025-05-26 DOI: 10.1016/j.ins.2025.122336

Michael-Sam Vidza , Marcin Budka , Wei Koong Chai , Mark Thrush , Mickaël Teixeira Alves

{"title":"Supply network disruption: A framework for assessing vulnerability and implementing resilience strategies","authors":"Michael-Sam Vidza , Marcin Budka , Wei Koong Chai , Mark Thrush , Mickaël Teixeira Alves","doi":"10.1016/j.ins.2025.122336","DOIUrl":"10.1016/j.ins.2025.122336","url":null,"abstract":"<div><div>Disruptions to food supply chains can have significant impacts on food security and economic stability. This study investigates the resilience of supply networks to such disruptions, focusing on the distribution of live fish between farms in England and Wales as a case study. A decision support framework is developed to assess network vulnerability and ensure operational continuity in the face of disruptions to the supply and demand balance. The framework incorporates a novel rewiring algorithm that dynamically reconfigures network connections to maintain the flow of goods. The algorithm predicts supply-demand pairs and adjusts connections to preserve functionality during disruptions. To evaluate the performance of the framework and algorithm, a combination of topological metrics, such as connectivity and redundancy, and operational measures, including supply fulfilment and distribution efficiency, is utilised. Through simulations of random and targeted node removals, the rewiring algorithm is shown to effectively mitigate the impact of disruptions, preserve network functionality, and help ensure a consistent supply of live fish. These findings offer valuable insights for managing disruptions in aquaculture supply chains and highlight the broader applicability of the framework to enhance the resilience of other supply networks.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"717 ","pages":"Article 122336"},"PeriodicalIF":8.1,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0